Student: Mark Khusid
Import Libraries¶
import pandas as pd
import matplotlib.pyplot as pltRead in Data¶
Read in CGM Sensor Data¶
CGMData_raw_df = pd.read_csv('CGMData.csv', low_memory=False)CGMData_raw_dfLoading...
Read in Insulin Pump Data¶
InsulinData_raw_df = pd.read_csv('InsulinData.csv', low_memory=False)InsulinData_raw_dfLoading...
Dataframe Preprocesing¶
Reverse Rows in CGM Sensor Dataframe¶
CGMData_reversed_df = CGMData_raw_df.iloc[::-1].reset_index(drop=True)
CGMData_reversed_dfLoading...
Reverse Rows in Insulin Pump Dataframe¶
InsulinData_reversed_df = InsulinData_raw_df.iloc[::-1].reset_index(drop=True)
InsulinData_reversed_dfLoading...
Create New Dataframes that Contains Only the Columns of Interest¶
CGMData_reversed_df.columnsIndex(['Index', 'Date', 'Time', 'New Device Time', 'BG Reading (mg/dL)',
'Linked BG Meter ID', 'Basal Rate (U/h)', 'Temp Basal Amount',
'Temp Basal Type', 'Temp Basal Duration (h:mm:ss)', 'Bolus Type',
'Bolus Volume Selected (U)', 'Bolus Volume Delivered (U)',
'Bolus Duration (h:mm:ss)', 'Prime Type', 'Prime Volume Delivered (U)',
'Alarm', 'Suspend', 'Rewind', 'BWZ Estimate (U)',
'BWZ Target High BG (mg/dL)', 'BWZ Target Low BG (mg/dL)',
'BWZ Carb Ratio (g/U)', 'BWZ Insulin Sensitivity (mg/dL/U)',
'BWZ Carb Input (grams)', 'BWZ BG Input (mg/dL)',
'BWZ Correction Estimate (U)', 'BWZ Food Estimate (U)',
'BWZ Active Insulin (U)', 'Sensor Calibration BG (mg/dL)',
'Sensor Glucose (mg/dL)', 'ISIG Value', 'Event Marker', 'Bolus Number',
'Bolus Cancellation Reason', 'BWZ Unabsorbed Insulin Total (U)',
'Final Bolus Estimate', 'Scroll Step Size', 'Insulin Action Curve Time',
'Sensor Calibration Rejected Reason', 'Preset Bolus', 'Bolus Source',
'Network Device Associated Reason',
'Network Device Disassociated Reason',
'Network Device Disconnected Reason', 'Sensor Exception',
'Preset Temp Basal Name'],
dtype='object')CGMData_reversed_dropped_columns_df = CGMData_reversed_df[['Date', 'Time', 'Sensor Glucose (mg/dL)', 'ISIG Value']]
CGMData_reversed_dropped_columns_dfLoading...
InsulinData_reversed_df.columnsIndex(['Index', 'Date', 'Time', 'New Device Time', 'BG Reading (mg/dL)',
'Linked BG Meter ID', 'Basal Rate (U/h)', 'Temp Basal Amount',
'Temp Basal Type', 'Temp Basal Duration (h:mm:ss)', 'Bolus Type',
'Bolus Volume Selected (U)', 'Bolus Volume Delivered (U)',
'Bolus Duration (h:mm:ss)', 'Prime Type', 'Prime Volume Delivered (U)',
'Alarm', 'Suspend', 'Rewind', 'BWZ Estimate (U)',
'BWZ Target High BG (mg/dL)', 'BWZ Target Low BG (mg/dL)',
'BWZ Carb Ratio (g/U)', 'BWZ Insulin Sensitivity (mg/dL/U)',
'BWZ Carb Input (grams)', 'BWZ BG Input (mg/dL)',
'BWZ Correction Estimate (U)', 'BWZ Food Estimate (U)',
'BWZ Active Insulin (U)', 'Sensor Calibration BG (mg/dL)',
'Sensor Glucose (mg/dL)', 'ISIG Value', 'Event Marker', 'Bolus Number',
'Bolus Cancellation Reason', 'BWZ Unabsorbed Insulin Total (U)',
'Final Bolus Estimate', 'Scroll Step Size', 'Insulin Action Curve Time',
'Sensor Calibration Rejected Reason', 'Preset Bolus', 'Bolus Source',
'Network Device Associated Reason',
'Network Device Disassociated Reason',
'Network Device Disconnected Reason', 'Sensor Exception',
'Preset Temp Basal Name'],
dtype='object')InsulinData_reversed_columns_dropped_df = InsulinData_reversed_df[['Date', 'Time', 'Alarm']]
InsulinData_reversed_columns_dropped_dfLoading...
Assign New Names to Dataframes to Faciliate Referencing¶
CGMData_df = CGMData_reversed_dropped_columns_df.copy(deep=True)
CGMData_dfLoading...
InsulinData_df = InsulinData_reversed_columns_dropped_df.copy(deep=True)
InsulinData_dfLoading...
Exploratory Data Analysis¶
EDA on CGM Data¶
CGMData_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 55343 entries, 0 to 55342
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 55343 non-null object
1 Time 55343 non-null object
2 Sensor Glucose (mg/dL) 51175 non-null float64
3 ISIG Value 51175 non-null float64
dtypes: float64(2), object(2)
memory usage: 1.7+ MB
CGMData_df.describe()Loading...
CGMData_df.indexRangeIndex(start=0, stop=55343, step=1)Visualize CGM Data¶
ax = CGMData_df.plot(
y = 'Sensor Glucose (mg/dL)',
use_index = True,
figsize = (12,4)
)
ax.set_title('CGM Sensor Glucose vs Row Number')
ax.set_xlabel('Row Number [count]')
ax.set_ylabel('Sensor Glucose [mg/dL]')
plt.show(block=False)
ax = CGMData_df.plot(
y = 'ISIG Value',
use_index = True,
figsize = (12,4)
)
ax.set_xlabel('Index Number [count]')
ax.set_ylabel('ISIG Value [raw]')
plt.show(block=False)
EDA on Insulin Data¶
InsulinData_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 41435 entries, 0 to 41434
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Date 41435 non-null object
1 Time 41435 non-null object
2 Alarm 3535 non-null object
dtypes: object(3)
memory usage: 971.3+ KB
InsulinData_df.describe()Loading...
Process the Data and Time Columns into Day Numbers¶
Add Datetime Column and Day Number to CGM Dataframe¶
CGMData_df.head(5)Loading...
CGMData_df['datetime'] = \
pd.to_datetime(
CGMData_df['Date'] + ' ' + CGMData_df['Time'],
format='%m/%d/%Y %H:%M:%S')
CGMData_dfLoading...
Add Datetime Column and Day Number to Insulin Pump Dataframe¶
InsulinData_df.head(5)Loading...
InsulinData_df['datetime'] = \
pd.to_datetime(
InsulinData_df['Date'] + ' ' + InsulinData_df['Time'],
format='%m/%d/%Y %H:%M:%S')
InsulinData_dfLoading...
Determine Start of Auto Mode in Insulin Pump Data¶
search_string = 'AUTO MODE ACTIVE PLGM OFF'
#first_occurence_index = InsulinData_df['Alarm'].str.contains(search_string).idxmax()
mask = InsulinData_df['Alarm'].str.contains(search_string, na=False)
if mask.any():
first_occurence_index = InsulinData_df[mask].index[0]
first_occurence_indexnp.int64(1303)InsulinData_df[first_occurence_index:first_occurence_index+1]Loading...
automode_Insulin_timestamp = InsulinData_df.iloc[first_occurence_index]['datetime']
automode_Insulin_timestampTimestamp('2017-08-09 08:07:13')Find Timestamp in CGM Data that is Nearest to Auto Mode in Insulin Pump Data¶
CGMData_df[CGMData_df['datetime'] > automode_Insulin_timestamp]Loading...
# Used .item() at end to convert to python int because result was a numpy int64
automode_CGM_time_stamp_index = CGMData_df[CGMData_df['datetime'] > automode_Insulin_timestamp].index[0].item()
automode_CGM_time_stamp_index4256CGMData_df[automode_CGM_time_stamp_index:automode_CGM_time_stamp_index+1]Loading...
Add Day Number Column to CGM Dataframe¶
#CGMData_df['datetime'].dt.floor('D')CGMData_df['Day Number'] = \
(CGMData_df['datetime'].dt.floor('D') - CGMData_df['datetime'].dt.floor('D').iloc[0]).dt.days + 1# See if the day number column is correct
pd.set_option('display.max_rows', 200)
#CGMData_df[:200]#CGMData_df.info()CGMData_dfLoading...
Assign Timestamps to Either Daytime or Overnight¶
# Define helper function that looks at the hour in a row datetime object
# and returns whether it is a daytime row or overnight row
def categorize_time(row):
hour = row.hour
if 6 <= hour < 24:
return 'daytime'
else:
return 'overnight'# Apply the function to each row and add to the time_interval column
CGMData_df['Time Interval'] = \
CGMData_df['datetime'].apply(categorize_time)#CGMData_df[200:300]Assign Manual or Auto Mode to Each Row in the CGM Dataframe¶
CGMData_df['Sensor Mode'] = \
['Manual Mode'
if i < automode_CGM_time_stamp_index
else 'Auto Mode'
for i in CGMData_df.index]CGMData_dfLoading...
Define Number of Datapoints per Day¶
five_minute_data_points_per_data = (24 * 60 ) / 5 # (24 hrs * 60 min / hr) / 5 mins
five_minute_data_points_per_data288.0Interpolate the Missing Sensor Data in the CGM Dataframe¶
CGMData_df['Sensor Glucose (mg/dL) [Interpolated]'] = \
CGMData_df['Sensor Glucose (mg/dL)'].interpolate(method='linear')CGMData_dfLoading...
Classify Glucose Levels by Metrics Table¶
# Define helper function for non secondary
def classify_sensor_glucose_level_non_sec(value):
if value > 250:
return 'hyperglycemia critical'
elif value > 180:
return 'hyperglycemia'
elif 70 <= value <= 180:
return 'normal'
elif 54 < value < 70:
return 'hypoglycemia level 1'
elif value <= 54:
return 'hypoglycemia level 2'
else:
return 'unknown'# Define helper function for secondary
def classify_sensor_glucose_level_sec(value):
if 70 <= value <= 150:
return 'secondary'
else:
return 'none'CGMData_df['Sensor Glucose Classification'] = \
CGMData_df['Sensor Glucose (mg/dL) [Interpolated]'].apply(classify_sensor_glucose_level_non_sec)CGMData_df['Sensor Glucose Classification (Secondary)'] = \
CGMData_df['Sensor Glucose (mg/dL) [Interpolated]'].apply(classify_sensor_glucose_level_sec)CGMData_dfLoading...
Rearrange Order of Columns in CGM Dataframe¶
CGMData_df = CGMData_df[[
'Date',
'Time',
'datetime',
'Day Number',
'Time Interval',
'Sensor Mode',
'Sensor Glucose (mg/dL)',
'Sensor Glucose (mg/dL) [Interpolated]',
'Sensor Glucose Classification',
'Sensor Glucose Classification (Secondary)',
'ISIG Value']]CGMData_dfLoading...
Calculations of Means¶
number_of_days_in_data = CGMData_df['Day Number'].max()
number_of_days_in_datanp.int64(203)Manual Mode / Overnight / Hyperglycemia¶
CGMData_manual_overnight_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_manual_overnight_hyper_df.describe()Loading...
CGMData_manual_overnight_hyper_df[0:5]Loading...
CGMData_manual_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 46
1 30
2 17
3 12
4 26
5 3
6 32
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_overnight_hyper_percentages_per_day_series = \
CGMData_manual_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_hyper_percentages_per_day_series0 15.972222
1 10.416667
2 5.902778
3 4.166667
4 9.027778
5 1.041667
6 11.111111
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_overnight_hyper_percentages_per_day_array = \
CGMData_manual_overnight_hyper_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_hyper_percentages_per_day_arrayarray([15.97222222, 10.41666667, 5.90277778, 4.16666667, 9.02777778,
1.04166667, 11.11111111])CGMData_manual_overnight_hyper_average_percentages_over_all_days = \
CGMData_manual_overnight_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_hyper_average_percentages_over_all_days.item()0.28393541324575805Auto Mode / Overnight / Hyperglycemia¶
CGMData_auto_overnight_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_auto_overnight_hyper_df.describe()Loading...
CGMData_auto_overnight_hyper_df[0:5]Loading...
CGMData_auto_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 39
1 15
2 17
3 4
4 45
5 18
6 37
7 5
8 2
9 13
10 1
11 7
12 5
13 23
14 18
15 16
16 1
17 52
18 62
19 19
20 3
21 58
22 8
23 2
24 6
25 14
26 18
27 1
28 10
29 1
30 4
31 21
32 10
33 11
34 24
35 5
36 9
37 9
38 14
39 39
40 18
41 41
42 8
43 32
44 16
45 5
46 3
47 2
48 4
49 13
50 5
51 17
52 12
53 9
54 24
55 8
56 12
57 14
58 5
59 27
60 20
61 7
62 35
63 24
64 8
65 8
66 6
67 2
68 13
69 22
70 8
71 7
72 31
73 26
74 38
75 24
76 22
77 4
78 2
79 4
80 13
81 7
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_hyper_percentages_per_day_series = \
CGMData_auto_overnight_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_hyper_percentages_per_day_series0 13.541667
1 5.208333
2 5.902778
3 1.388889
4 15.625000
5 6.250000
6 12.847222
7 1.736111
8 0.694444
9 4.513889
10 0.347222
11 2.430556
12 1.736111
13 7.986111
14 6.250000
15 5.555556
16 0.347222
17 18.055556
18 21.527778
19 6.597222
20 1.041667
21 20.138889
22 2.777778
23 0.694444
24 2.083333
25 4.861111
26 6.250000
27 0.347222
28 3.472222
29 0.347222
30 1.388889
31 7.291667
32 3.472222
33 3.819444
34 8.333333
35 1.736111
36 3.125000
37 3.125000
38 4.861111
39 13.541667
40 6.250000
41 14.236111
42 2.777778
43 11.111111
44 5.555556
45 1.736111
46 1.041667
47 0.694444
48 1.388889
49 4.513889
50 1.736111
51 5.902778
52 4.166667
53 3.125000
54 8.333333
55 2.777778
56 4.166667
57 4.861111
58 1.736111
59 9.375000
60 6.944444
61 2.430556
62 12.152778
63 8.333333
64 2.777778
65 2.777778
66 2.083333
67 0.694444
68 4.513889
69 7.638889
70 2.777778
71 2.430556
72 10.763889
73 9.027778
74 13.194444
75 8.333333
76 7.638889
77 1.388889
78 0.694444
79 1.388889
80 4.513889
81 2.430556
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_hyper_percentages_per_day_array = \
CGMData_auto_overnight_hyper_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_hyper_percentages_per_day_arrayarray([13.54166667, 5.20833333, 5.90277778, 1.38888889, 15.625 ,
6.25 , 12.84722222, 1.73611111, 0.69444444, 4.51388889,
0.34722222, 2.43055556, 1.73611111, 7.98611111, 6.25 ,
5.55555556, 0.34722222, 18.05555556, 21.52777778, 6.59722222,
1.04166667, 20.13888889, 2.77777778, 0.69444444, 2.08333333,
4.86111111, 6.25 , 0.34722222, 3.47222222, 0.34722222,
1.38888889, 7.29166667, 3.47222222, 3.81944444, 8.33333333,
1.73611111, 3.125 , 3.125 , 4.86111111, 13.54166667,
6.25 , 14.23611111, 2.77777778, 11.11111111, 5.55555556,
1.73611111, 1.04166667, 0.69444444, 1.38888889, 4.51388889,
1.73611111, 5.90277778, 4.16666667, 3.125 , 8.33333333,
2.77777778, 4.16666667, 4.86111111, 1.73611111, 9.375 ,
6.94444444, 2.43055556, 12.15277778, 8.33333333, 2.77777778,
2.77777778, 2.08333333, 0.69444444, 4.51388889, 7.63888889,
2.77777778, 2.43055556, 10.76388889, 9.02777778, 13.19444444,
8.33333333, 7.63888889, 1.38888889, 0.69444444, 1.38888889,
4.51388889, 2.43055556])CGMData_auto_overnight_hyper_average_percentages_over_all_days = \
CGMData_auto_overnight_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_hyper_average_percentages_over_all_days.item()2.1756978653530377Manual Mode / Overnight / Hyperglycemia Critical¶
CGMData_manual_overnight_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_manual_overnight_hyper_crit_df.describe()Loading...
CGMData_manual_overnight_hyper_crit_df[0:50]Loading...
CGMData_manual_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 16
1 29
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_overnight_hyper_crit_percentages_per_day_series = \
CGMData_manual_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_hyper_crit_percentages_per_day_series0 5.555556
1 10.069444
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_overnight_hyper_crit_percentages_per_day_array = \
CGMData_manual_overnight_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_hyper_crit_percentages_per_day_arrayarray([ 5.55555556, 10.06944444])CGMData_manual_overnight_hyper_crit_average_percentages_over_all_days = \
CGMData_manual_overnight_hyper_crit_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_hyper_crit_average_percentages_over_all_days.item()0.0769704433497537Auto Mode / Overnight / Hyperglycemia Critical¶
CGMData_auto_overnight_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_auto_overnight_hyper_crit_df.describe()Loading...
CGMData_auto_overnight_hyper_crit_df[0:5]Loading...
CGMData_auto_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 12
1 25
2 1
3 31
4 6
5 7
6 2
7 7
8 14
9 1
10 3
11 6
12 4
13 2
14 11
15 5
16 7
17 28
18 8
19 3
20 11
21 2
22 23
23 2
24 6
25 10
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_hyper_crit_percentages_per_day_series = \
CGMData_auto_overnight_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_hyper_crit_percentages_per_day_series0 4.166667
1 8.680556
2 0.347222
3 10.763889
4 2.083333
5 2.430556
6 0.694444
7 2.430556
8 4.861111
9 0.347222
10 1.041667
11 2.083333
12 1.388889
13 0.694444
14 3.819444
15 1.736111
16 2.430556
17 9.722222
18 2.777778
19 1.041667
20 3.819444
21 0.694444
22 7.986111
23 0.694444
24 2.083333
25 3.472222
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_hyper_crit_percentages_per_day_array = \
CGMData_auto_overnight_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_hyper_crit_percentages_per_day_arrayarray([ 4.16666667, 8.68055556, 0.34722222, 10.76388889, 2.08333333,
2.43055556, 0.69444444, 2.43055556, 4.86111111, 0.34722222,
1.04166667, 2.08333333, 1.38888889, 0.69444444, 3.81944444,
1.73611111, 2.43055556, 9.72222222, 2.77777778, 1.04166667,
3.81944444, 0.69444444, 7.98611111, 0.69444444, 2.08333333,
3.47222222])CGMData_auto_overnight_hyper_crit_average_percentages_over_all_days = \
CGMData_auto_overnight_hyper_crit_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_hyper_crit_average_percentages_over_all_days.item()0.4053776683087028Manual Mode / Overnight / Normal¶
CGMData_manual_overnight_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_manual_overnight_normal_df.describe()Loading...
CGMData_manual_overnight_normal_df[0:50]Loading...
CGMData_manual_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 68
1 26
2 63
3 42
4 72
5 39
6 60
7 72
8 17
9 72
10 69
11 56
12 72
13 72
14 40
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_overnight_normal_percentages_per_day_series = \
CGMData_manual_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_normal_percentages_per_day_series0 23.611111
1 9.027778
2 21.875000
3 14.583333
4 25.000000
5 13.541667
6 20.833333
7 25.000000
8 5.902778
9 25.000000
10 23.958333
11 19.444444
12 25.000000
13 25.000000
14 13.888889
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_overnight_normal_percentages_per_day_array = \
CGMData_manual_overnight_normal_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_normal_percentages_per_day_arrayarray([23.61111111, 9.02777778, 21.875 , 14.58333333, 25. ,
13.54166667, 20.83333333, 25. , 5.90277778, 25. ,
23.95833333, 19.44444444, 25. , 25. , 13.88888889])CGMData_manual_overnight_normal_average_percentages_over_all_days = \
CGMData_manual_overnight_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_normal_average_percentages_over_all_days.item()1.4367816091954027Auto Mode / Overnight / Normal¶
CGMData_auto_overnight_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_auto_overnight_normal_df.describe()Loading...
CGMData_auto_overnight_normal_df[0:5]Loading...
CGMData_auto_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 21
1 60
2 72
3 72
4 73
5 72
6 51
7 71
8 72
9 57
10 72
11 30
12 72
13 68
14 27
15 72
16 72
17 72
18 54
19 34
20 59
21 72
22 70
23 72
24 70
25 28
26 71
27 65
28 72
29 72
30 70
31 72
32 72
33 72
34 67
35 49
36 72
37 72
38 72
39 72
40 72
41 72
42 72
43 72
44 72
45 26
46 72
47 72
48 49
49 72
50 71
51 20
52 8
53 70
54 72
55 72
56 53
57 69
58 72
59 6
60 72
61 71
62 72
63 72
64 72
65 72
66 62
67 60
68 72
69 72
70 69
71 72
72 64
73 58
74 62
75 58
76 72
77 46
78 72
79 44
80 22
81 68
82 71
83 72
84 72
85 72
86 72
87 75
88 71
89 68
90 39
91 59
92 55
93 72
94 72
95 30
96 68
97 72
98 63
99 63
100 72
101 72
102 72
103 72
104 61
105 48
106 33
107 72
108 72
109 72
110 72
111 54
112 72
113 72
114 72
115 72
116 72
117 72
118 72
119 24
120 64
121 64
122 38
123 52
124 72
125 67
126 72
127 63
128 72
129 65
130 60
131 59
132 67
133 72
134 55
135 60
136 44
137 72
138 72
139 48
140 58
141 53
142 72
143 30
144 61
145 67
146 37
147 35
148 72
149 65
150 33
151 45
152 59
153 72
154 72
155 64
156 72
157 45
158 70
159 59
160 72
161 50
162 69
163 64
164 72
165 63
166 41
167 23
168 34
169 48
170 72
171 50
172 61
173 64
174 67
175 59
176 69
177 40
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_normal_percentages_per_day_series = \
CGMData_auto_overnight_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_normal_percentages_per_day_series0 7.291667
1 20.833333
2 25.000000
3 25.000000
4 25.347222
5 25.000000
6 17.708333
7 24.652778
8 25.000000
9 19.791667
10 25.000000
11 10.416667
12 25.000000
13 23.611111
14 9.375000
15 25.000000
16 25.000000
17 25.000000
18 18.750000
19 11.805556
20 20.486111
21 25.000000
22 24.305556
23 25.000000
24 24.305556
25 9.722222
26 24.652778
27 22.569444
28 25.000000
29 25.000000
30 24.305556
31 25.000000
32 25.000000
33 25.000000
34 23.263889
35 17.013889
36 25.000000
37 25.000000
38 25.000000
39 25.000000
40 25.000000
41 25.000000
42 25.000000
43 25.000000
44 25.000000
45 9.027778
46 25.000000
47 25.000000
48 17.013889
49 25.000000
50 24.652778
51 6.944444
52 2.777778
53 24.305556
54 25.000000
55 25.000000
56 18.402778
57 23.958333
58 25.000000
59 2.083333
60 25.000000
61 24.652778
62 25.000000
63 25.000000
64 25.000000
65 25.000000
66 21.527778
67 20.833333
68 25.000000
69 25.000000
70 23.958333
71 25.000000
72 22.222222
73 20.138889
74 21.527778
75 20.138889
76 25.000000
77 15.972222
78 25.000000
79 15.277778
80 7.638889
81 23.611111
82 24.652778
83 25.000000
84 25.000000
85 25.000000
86 25.000000
87 26.041667
88 24.652778
89 23.611111
90 13.541667
91 20.486111
92 19.097222
93 25.000000
94 25.000000
95 10.416667
96 23.611111
97 25.000000
98 21.875000
99 21.875000
100 25.000000
101 25.000000
102 25.000000
103 25.000000
104 21.180556
105 16.666667
106 11.458333
107 25.000000
108 25.000000
109 25.000000
110 25.000000
111 18.750000
112 25.000000
113 25.000000
114 25.000000
115 25.000000
116 25.000000
117 25.000000
118 25.000000
119 8.333333
120 22.222222
121 22.222222
122 13.194444
123 18.055556
124 25.000000
125 23.263889
126 25.000000
127 21.875000
128 25.000000
129 22.569444
130 20.833333
131 20.486111
132 23.263889
133 25.000000
134 19.097222
135 20.833333
136 15.277778
137 25.000000
138 25.000000
139 16.666667
140 20.138889
141 18.402778
142 25.000000
143 10.416667
144 21.180556
145 23.263889
146 12.847222
147 12.152778
148 25.000000
149 22.569444
150 11.458333
151 15.625000
152 20.486111
153 25.000000
154 25.000000
155 22.222222
156 25.000000
157 15.625000
158 24.305556
159 20.486111
160 25.000000
161 17.361111
162 23.958333
163 22.222222
164 25.000000
165 21.875000
166 14.236111
167 7.986111
168 11.805556
169 16.666667
170 25.000000
171 17.361111
172 21.180556
173 22.222222
174 23.263889
175 20.486111
176 23.958333
177 13.888889
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_normal_percentages_per_day_array = \
CGMData_auto_overnight_normal_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_normal_percentages_per_day_arrayarray([ 7.29166667, 20.83333333, 25. , 25. , 25.34722222,
25. , 17.70833333, 24.65277778, 25. , 19.79166667,
25. , 10.41666667, 25. , 23.61111111, 9.375 ,
25. , 25. , 25. , 18.75 , 11.80555556,
20.48611111, 25. , 24.30555556, 25. , 24.30555556,
9.72222222, 24.65277778, 22.56944444, 25. , 25. ,
24.30555556, 25. , 25. , 25. , 23.26388889,
17.01388889, 25. , 25. , 25. , 25. ,
25. , 25. , 25. , 25. , 25. ,
9.02777778, 25. , 25. , 17.01388889, 25. ,
24.65277778, 6.94444444, 2.77777778, 24.30555556, 25. ,
25. , 18.40277778, 23.95833333, 25. , 2.08333333,
25. , 24.65277778, 25. , 25. , 25. ,
25. , 21.52777778, 20.83333333, 25. , 25. ,
23.95833333, 25. , 22.22222222, 20.13888889, 21.52777778,
20.13888889, 25. , 15.97222222, 25. , 15.27777778,
7.63888889, 23.61111111, 24.65277778, 25. , 25. ,
25. , 25. , 26.04166667, 24.65277778, 23.61111111,
13.54166667, 20.48611111, 19.09722222, 25. , 25. ,
10.41666667, 23.61111111, 25. , 21.875 , 21.875 ,
25. , 25. , 25. , 25. , 21.18055556,
16.66666667, 11.45833333, 25. , 25. , 25. ,
25. , 18.75 , 25. , 25. , 25. ,
25. , 25. , 25. , 25. , 8.33333333,
22.22222222, 22.22222222, 13.19444444, 18.05555556, 25. ,
23.26388889, 25. , 21.875 , 25. , 22.56944444,
20.83333333, 20.48611111, 23.26388889, 25. , 19.09722222,
20.83333333, 15.27777778, 25. , 25. , 16.66666667,
20.13888889, 18.40277778, 25. , 10.41666667, 21.18055556,
23.26388889, 12.84722222, 12.15277778, 25. , 22.56944444,
11.45833333, 15.625 , 20.48611111, 25. , 25. ,
22.22222222, 25. , 15.625 , 24.30555556, 20.48611111,
25. , 17.36111111, 23.95833333, 22.22222222, 25. ,
21.875 , 14.23611111, 7.98611111, 11.80555556, 16.66666667,
25. , 17.36111111, 21.18055556, 22.22222222, 23.26388889,
20.48611111, 23.95833333, 13.88888889])CGMData_auto_overnight_normal_average_percentages_over_all_days = \
CGMData_auto_overnight_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_normal_average_percentages_over_all_days.item()18.756841817186643Manual Mode / Overnight / Secondary¶
CGMData_manual_overnight_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_manual_overnight_secondary_df.describe()Loading...
CGMData_manual_overnight_secondary_df[0:50]Loading...
CGMData_manual_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 57
1 63
2 19
3 55
4 23
5 26
6 64
7 64
8 44
9 54
10 72
11 52
12 32
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_overnight_secondary_percentages_per_day_series = \
CGMData_manual_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_secondary_percentages_per_day_series0 19.791667
1 21.875000
2 6.597222
3 19.097222
4 7.986111
5 9.027778
6 22.222222
7 22.222222
8 15.277778
9 18.750000
10 25.000000
11 18.055556
12 11.111111
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_overnight_secondary_percentages_per_day_array = \
CGMData_manual_overnight_secondary_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_secondary_percentages_per_day_arrayarray([19.79166667, 21.875 , 6.59722222, 19.09722222, 7.98611111,
9.02777778, 22.22222222, 22.22222222, 15.27777778, 18.75 ,
25. , 18.05555556, 11.11111111])CGMData_manual_overnight_secondary_average_percentages_over_all_days = \
CGMData_manual_overnight_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_secondary_average_percentages_over_all_days.item()1.0690339354132457Auto Mode / Overnight / Secondary¶
CGMData_auto_overnight_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_auto_overnight_secondary_df.describe()Loading...
CGMData_auto_overnight_secondary_df[0:50]Loading...
CGMData_auto_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 16
1 42
2 72
3 70
4 48
5 72
6 51
7 71
8 30
9 42
10 49
11 21
12 72
13 67
14 17
15 72
16 72
17 68
18 39
19 22
20 44
21 72
22 67
23 72
24 57
25 22
26 64
27 53
28 70
29 67
30 69
31 72
32 71
33 72
34 57
35 36
36 65
37 72
38 63
39 72
40 66
41 68
42 67
43 72
44 72
45 21
46 72
47 72
48 47
49 65
50 58
51 13
52 54
53 71
54 71
55 27
56 62
57 72
58 72
59 71
60 54
61 47
62 72
63 72
64 62
65 60
66 72
67 72
68 54
69 63
70 28
71 58
72 58
73 46
74 72
75 43
76 68
77 41
78 16
79 68
80 65
81 69
82 62
83 68
84 72
85 64
86 65
87 55
88 21
89 48
90 38
91 72
92 64
93 24
94 68
95 72
96 60
97 51
98 72
99 72
100 68
101 65
102 57
103 37
104 72
105 72
106 67
107 55
108 43
109 72
110 45
111 56
112 72
113 60
114 72
115 67
116 19
117 53
118 60
119 31
120 35
121 72
122 52
123 67
124 45
125 62
126 48
127 41
128 56
129 62
130 58
131 39
132 43
133 41
134 64
135 67
136 28
137 53
138 32
139 72
140 28
141 39
142 61
143 28
144 28
145 72
146 59
147 31
148 26
149 46
150 55
151 58
152 50
153 72
154 37
155 50
156 49
157 72
158 39
159 66
160 58
161 68
162 61
163 35
164 15
165 8
166 40
167 65
168 34
169 58
170 63
171 38
172 29
173 69
174 38
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_secondary_percentages_per_day_series = \
CGMData_auto_overnight_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_secondary_percentages_per_day_series0 5.555556
1 14.583333
2 25.000000
3 24.305556
4 16.666667
5 25.000000
6 17.708333
7 24.652778
8 10.416667
9 14.583333
10 17.013889
11 7.291667
12 25.000000
13 23.263889
14 5.902778
15 25.000000
16 25.000000
17 23.611111
18 13.541667
19 7.638889
20 15.277778
21 25.000000
22 23.263889
23 25.000000
24 19.791667
25 7.638889
26 22.222222
27 18.402778
28 24.305556
29 23.263889
30 23.958333
31 25.000000
32 24.652778
33 25.000000
34 19.791667
35 12.500000
36 22.569444
37 25.000000
38 21.875000
39 25.000000
40 22.916667
41 23.611111
42 23.263889
43 25.000000
44 25.000000
45 7.291667
46 25.000000
47 25.000000
48 16.319444
49 22.569444
50 20.138889
51 4.513889
52 18.750000
53 24.652778
54 24.652778
55 9.375000
56 21.527778
57 25.000000
58 25.000000
59 24.652778
60 18.750000
61 16.319444
62 25.000000
63 25.000000
64 21.527778
65 20.833333
66 25.000000
67 25.000000
68 18.750000
69 21.875000
70 9.722222
71 20.138889
72 20.138889
73 15.972222
74 25.000000
75 14.930556
76 23.611111
77 14.236111
78 5.555556
79 23.611111
80 22.569444
81 23.958333
82 21.527778
83 23.611111
84 25.000000
85 22.222222
86 22.569444
87 19.097222
88 7.291667
89 16.666667
90 13.194444
91 25.000000
92 22.222222
93 8.333333
94 23.611111
95 25.000000
96 20.833333
97 17.708333
98 25.000000
99 25.000000
100 23.611111
101 22.569444
102 19.791667
103 12.847222
104 25.000000
105 25.000000
106 23.263889
107 19.097222
108 14.930556
109 25.000000
110 15.625000
111 19.444444
112 25.000000
113 20.833333
114 25.000000
115 23.263889
116 6.597222
117 18.402778
118 20.833333
119 10.763889
120 12.152778
121 25.000000
122 18.055556
123 23.263889
124 15.625000
125 21.527778
126 16.666667
127 14.236111
128 19.444444
129 21.527778
130 20.138889
131 13.541667
132 14.930556
133 14.236111
134 22.222222
135 23.263889
136 9.722222
137 18.402778
138 11.111111
139 25.000000
140 9.722222
141 13.541667
142 21.180556
143 9.722222
144 9.722222
145 25.000000
146 20.486111
147 10.763889
148 9.027778
149 15.972222
150 19.097222
151 20.138889
152 17.361111
153 25.000000
154 12.847222
155 17.361111
156 17.013889
157 25.000000
158 13.541667
159 22.916667
160 20.138889
161 23.611111
162 21.180556
163 12.152778
164 5.208333
165 2.777778
166 13.888889
167 22.569444
168 11.805556
169 20.138889
170 21.875000
171 13.194444
172 10.069444
173 23.958333
174 13.194444
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_secondary_percentages_per_day_array = \
CGMData_auto_overnight_secondary_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_secondary_percentages_per_day_arrayarray([ 5.55555556, 14.58333333, 25. , 24.30555556, 16.66666667,
25. , 17.70833333, 24.65277778, 10.41666667, 14.58333333,
17.01388889, 7.29166667, 25. , 23.26388889, 5.90277778,
25. , 25. , 23.61111111, 13.54166667, 7.63888889,
15.27777778, 25. , 23.26388889, 25. , 19.79166667,
7.63888889, 22.22222222, 18.40277778, 24.30555556, 23.26388889,
23.95833333, 25. , 24.65277778, 25. , 19.79166667,
12.5 , 22.56944444, 25. , 21.875 , 25. ,
22.91666667, 23.61111111, 23.26388889, 25. , 25. ,
7.29166667, 25. , 25. , 16.31944444, 22.56944444,
20.13888889, 4.51388889, 18.75 , 24.65277778, 24.65277778,
9.375 , 21.52777778, 25. , 25. , 24.65277778,
18.75 , 16.31944444, 25. , 25. , 21.52777778,
20.83333333, 25. , 25. , 18.75 , 21.875 ,
9.72222222, 20.13888889, 20.13888889, 15.97222222, 25. ,
14.93055556, 23.61111111, 14.23611111, 5.55555556, 23.61111111,
22.56944444, 23.95833333, 21.52777778, 23.61111111, 25. ,
22.22222222, 22.56944444, 19.09722222, 7.29166667, 16.66666667,
13.19444444, 25. , 22.22222222, 8.33333333, 23.61111111,
25. , 20.83333333, 17.70833333, 25. , 25. ,
23.61111111, 22.56944444, 19.79166667, 12.84722222, 25. ,
25. , 23.26388889, 19.09722222, 14.93055556, 25. ,
15.625 , 19.44444444, 25. , 20.83333333, 25. ,
23.26388889, 6.59722222, 18.40277778, 20.83333333, 10.76388889,
12.15277778, 25. , 18.05555556, 23.26388889, 15.625 ,
21.52777778, 16.66666667, 14.23611111, 19.44444444, 21.52777778,
20.13888889, 13.54166667, 14.93055556, 14.23611111, 22.22222222,
23.26388889, 9.72222222, 18.40277778, 11.11111111, 25. ,
9.72222222, 13.54166667, 21.18055556, 9.72222222, 9.72222222,
25. , 20.48611111, 10.76388889, 9.02777778, 15.97222222,
19.09722222, 20.13888889, 17.36111111, 25. , 12.84722222,
17.36111111, 17.01388889, 25. , 13.54166667, 22.91666667,
20.13888889, 23.61111111, 21.18055556, 12.15277778, 5.20833333,
2.77777778, 13.88888889, 22.56944444, 11.80555556, 20.13888889,
21.875 , 13.19444444, 10.06944444, 23.95833333, 13.19444444])CGMData_auto_overnight_secondary_average_percentages_over_all_days = \
CGMData_auto_overnight_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_secondary_average_percentages_over_all_days.item()16.379310344827587Manual Mode / Overnight / Hypoglycemia Level 1¶
CGMData_manual_overnight_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_manual_overnight_hypo_1_df.describe()Loading...
CGMData_manual_overnight_hypo_1_df[0:50]Loading...
CGMData_manual_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 4
1 9
2 16
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_overnight_hypo_1_percentages_per_day_series = \
CGMData_manual_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_hypo_1_percentages_per_day_series0 1.388889
1 3.125000
2 5.555556
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_overnight_hypo_1_percentages_per_day_array = \
CGMData_manual_overnight_hypo_1_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_hypo_1_percentages_per_day_arrayarray([1.38888889, 3.125 , 5.55555556])CGMData_manual_overnight_hypo_1_average_percentages_over_all_days = \
CGMData_manual_overnight_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_hypo_1_average_percentages_over_all_days.item()0.04960317460317461Auto Mode / Overnight / Hypoglycemia Level 1¶
CGMData_auto_overnight_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_auto_overnight_hypo_1_df.describe()Loading...
CGMData_auto_overnight_hypo_1_df[0:5]Loading...
CGMData_auto_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 10
1 9
2 1
3 8
4 2
5 2
6 18
7 2
8 1
9 10
10 12
11 3
12 14
13 10
14 12
15 12
16 3
17 4
18 3
19 2
20 4
21 2
22 2
23 5
24 5
25 4
26 2
27 4
28 4
29 5
30 7
31 6
32 1
33 8
34 5
35 4
36 5
37 10
38 3
39 1
40 1
41 3
42 6
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_hypo_1_percentages_per_day_series = \
CGMData_auto_overnight_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_hypo_1_percentages_per_day_series0 3.472222
1 3.125000
2 0.347222
3 2.777778
4 0.694444
5 0.694444
6 6.250000
7 0.694444
8 0.347222
9 3.472222
10 4.166667
11 1.041667
12 4.861111
13 3.472222
14 4.166667
15 4.166667
16 1.041667
17 1.388889
18 1.041667
19 0.694444
20 1.388889
21 0.694444
22 0.694444
23 1.736111
24 1.736111
25 1.388889
26 0.694444
27 1.388889
28 1.388889
29 1.736111
30 2.430556
31 2.083333
32 0.347222
33 2.777778
34 1.736111
35 1.388889
36 1.736111
37 3.472222
38 1.041667
39 0.347222
40 0.347222
41 1.041667
42 2.083333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_hypo_1_percentages_per_day_array = \
CGMData_auto_overnight_hypo_1_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_hypo_1_percentages_per_day_arrayarray([3.47222222, 3.125 , 0.34722222, 2.77777778, 0.69444444,
0.69444444, 6.25 , 0.69444444, 0.34722222, 3.47222222,
4.16666667, 1.04166667, 4.86111111, 3.47222222, 4.16666667,
4.16666667, 1.04166667, 1.38888889, 1.04166667, 0.69444444,
1.38888889, 0.69444444, 0.69444444, 1.73611111, 1.73611111,
1.38888889, 0.69444444, 1.38888889, 1.38888889, 1.73611111,
2.43055556, 2.08333333, 0.34722222, 2.77777778, 1.73611111,
1.38888889, 1.73611111, 3.47222222, 1.04166667, 0.34722222,
0.34722222, 1.04166667, 2.08333333])CGMData_auto_overnight_hypo_1_average_percentages_over_all_days = \
CGMData_auto_overnight_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_hypo_1_average_percentages_over_all_days.item()0.4019567597153804Manual Mode / Overnight / Hypoglycemia Level 2¶
CGMData_manual_overnight_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_manual_overnight_hypo_2_df.describe()Loading...
CGMData_manual_overnight_hypo_2_df[0:50]Loading...
CGMData_manual_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']Series([], Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64)CGMData_manual_overnight_hypo_2_percentages_per_day_series = \
CGMData_manual_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_overnight_hypo_2_percentages_per_day_seriesSeries([], Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64)CGMData_manual_overnight_hypo_2_percentages_per_day_array = \
CGMData_manual_overnight_hypo_2_percentages_per_day_series.to_numpy()
CGMData_manual_overnight_hypo_2_percentages_per_day_arrayarray([], dtype=float64)CGMData_manual_overnight_hypo_2_average_percentages_over_all_days = \
CGMData_manual_overnight_hypo_2_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_overnight_hypo_2_average_percentages_over_all_days.item()0.0Auto Mode / Overnight / Hypoglycemia Level 2¶
CGMData_auto_overnight_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'overnight') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_auto_overnight_hypo_2_df.describe()Loading...
CGMData_auto_overnight_hypo_2_df[0:5]Loading...
CGMData_auto_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 2
1 12
2 4
3 1
4 28
5 9
6 12
7 2
8 5
9 4
10 2
11 1
12 2
13 3
14 12
15 4
16 9
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_overnight_hypo_2_percentages_per_day_series = \
CGMData_auto_overnight_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_overnight_hypo_2_percentages_per_day_series0 0.694444
1 4.166667
2 1.388889
3 0.347222
4 9.722222
5 3.125000
6 4.166667
7 0.694444
8 1.736111
9 1.388889
10 0.694444
11 0.347222
12 0.694444
13 1.041667
14 4.166667
15 1.388889
16 3.125000
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_overnight_hypo_2_percentages_per_day_array = \
CGMData_auto_overnight_hypo_2_percentages_per_day_series.to_numpy()
CGMData_auto_overnight_hypo_2_percentages_per_day_arrayarray([0.69444444, 4.16666667, 1.38888889, 0.34722222, 9.72222222,
3.125 , 4.16666667, 0.69444444, 1.73611111, 1.38888889,
0.69444444, 0.34722222, 0.69444444, 1.04166667, 4.16666667,
1.38888889, 3.125 ])CGMData_auto_overnight_hypo_2_average_percentages_over_all_days = \
CGMData_auto_overnight_hypo_2_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_overnight_hypo_2_average_percentages_over_all_days.item()0.19157088122605362Manual Mode / Daytime / Hyperglycemia¶
CGMData_manual_daytime_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_manual_daytime_hyper_df.describe()Loading...
CGMData_manual_daytime_hyper_df[0:5]Loading...
CGMData_manual_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 12
1 59
2 73
3 53
4 29
5 34
6 42
7 51
8 49
9 34
10 63
11 77
12 36
13 59
14 96
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_hyper_percentages_per_day_series = \
CGMData_manual_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_hyper_percentages_per_day_series0 4.166667
1 20.486111
2 25.347222
3 18.402778
4 10.069444
5 11.805556
6 14.583333
7 17.708333
8 17.013889
9 11.805556
10 21.875000
11 26.736111
12 12.500000
13 20.486111
14 33.333333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_hyper_percentages_per_day_array = \
CGMData_manual_daytime_hyper_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_hyper_percentages_per_day_arrayarray([ 4.16666667, 20.48611111, 25.34722222, 18.40277778, 10.06944444,
11.80555556, 14.58333333, 17.70833333, 17.01388889, 11.80555556,
21.875 , 26.73611111, 12.5 , 20.48611111, 33.33333333])CGMData_manual_daytime_hyper_average_percentages_over_all_days = \
CGMData_manual_daytime_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_daytime_hyper_average_percentages_over_all_days.item()1.3119184455391353Auto Mode / Daytime / Hyperglycemia¶
CGMData_auto_daytime_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_auto_daytime_hyper_df.describe()Loading...
CGMData_auto_daytime_hyper_df[0:5]Loading...
CGMData_auto_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 31
1 45
2 77
3 39
4 41
5 69
6 67
7 34
8 2
9 115
10 70
11 53
12 52
13 52
14 59
15 92
16 43
17 32
18 21
19 95
20 89
21 25
22 68
23 45
24 36
25 35
26 38
27 96
28 50
29 19
30 51
31 29
32 17
33 26
34 61
35 32
36 84
37 64
38 36
39 13
40 40
41 59
42 26
43 22
44 57
45 47
46 31
47 47
48 75
49 100
50 113
51 75
52 42
53 58
54 33
55 102
56 46
57 70
58 21
59 65
60 44
61 46
62 64
63 57
64 64
65 51
66 66
67 53
68 44
69 2
70 40
71 60
72 78
73 55
74 9
75 22
76 52
77 86
78 54
79 39
80 42
81 20
82 30
83 27
84 1
85 58
86 10
87 36
88 7
89 37
90 68
91 34
92 57
93 58
94 7
95 12
96 20
97 45
98 61
99 70
100 26
101 43
102 25
103 20
104 69
105 103
106 40
107 68
108 60
109 60
110 17
111 1
112 50
113 50
114 21
115 19
116 47
117 23
118 19
119 6
120 46
121 60
122 21
123 44
124 80
125 40
126 29
127 39
128 43
129 44
130 72
131 61
132 48
133 38
134 38
135 53
136 45
137 32
138 83
139 51
140 76
141 70
142 12
143 40
144 3
145 72
146 18
147 60
148 50
149 49
150 76
151 26
152 17
153 58
154 88
155 59
156 47
157 31
158 50
159 41
160 61
161 42
162 32
163 57
164 112
165 68
166 84
167 51
168 21
169 45
170 49
171 107
172 46
173 54
174 59
175 20
176 78
177 6
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_hyper_percentages_per_day_series = \
CGMData_auto_daytime_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_hyper_percentages_per_day_series0 10.763889
1 15.625000
2 26.736111
3 13.541667
4 14.236111
5 23.958333
6 23.263889
7 11.805556
8 0.694444
9 39.930556
10 24.305556
11 18.402778
12 18.055556
13 18.055556
14 20.486111
15 31.944444
16 14.930556
17 11.111111
18 7.291667
19 32.986111
20 30.902778
21 8.680556
22 23.611111
23 15.625000
24 12.500000
25 12.152778
26 13.194444
27 33.333333
28 17.361111
29 6.597222
30 17.708333
31 10.069444
32 5.902778
33 9.027778
34 21.180556
35 11.111111
36 29.166667
37 22.222222
38 12.500000
39 4.513889
40 13.888889
41 20.486111
42 9.027778
43 7.638889
44 19.791667
45 16.319444
46 10.763889
47 16.319444
48 26.041667
49 34.722222
50 39.236111
51 26.041667
52 14.583333
53 20.138889
54 11.458333
55 35.416667
56 15.972222
57 24.305556
58 7.291667
59 22.569444
60 15.277778
61 15.972222
62 22.222222
63 19.791667
64 22.222222
65 17.708333
66 22.916667
67 18.402778
68 15.277778
69 0.694444
70 13.888889
71 20.833333
72 27.083333
73 19.097222
74 3.125000
75 7.638889
76 18.055556
77 29.861111
78 18.750000
79 13.541667
80 14.583333
81 6.944444
82 10.416667
83 9.375000
84 0.347222
85 20.138889
86 3.472222
87 12.500000
88 2.430556
89 12.847222
90 23.611111
91 11.805556
92 19.791667
93 20.138889
94 2.430556
95 4.166667
96 6.944444
97 15.625000
98 21.180556
99 24.305556
100 9.027778
101 14.930556
102 8.680556
103 6.944444
104 23.958333
105 35.763889
106 13.888889
107 23.611111
108 20.833333
109 20.833333
110 5.902778
111 0.347222
112 17.361111
113 17.361111
114 7.291667
115 6.597222
116 16.319444
117 7.986111
118 6.597222
119 2.083333
120 15.972222
121 20.833333
122 7.291667
123 15.277778
124 27.777778
125 13.888889
126 10.069444
127 13.541667
128 14.930556
129 15.277778
130 25.000000
131 21.180556
132 16.666667
133 13.194444
134 13.194444
135 18.402778
136 15.625000
137 11.111111
138 28.819444
139 17.708333
140 26.388889
141 24.305556
142 4.166667
143 13.888889
144 1.041667
145 25.000000
146 6.250000
147 20.833333
148 17.361111
149 17.013889
150 26.388889
151 9.027778
152 5.902778
153 20.138889
154 30.555556
155 20.486111
156 16.319444
157 10.763889
158 17.361111
159 14.236111
160 21.180556
161 14.583333
162 11.111111
163 19.791667
164 38.888889
165 23.611111
166 29.166667
167 17.708333
168 7.291667
169 15.625000
170 17.013889
171 37.152778
172 15.972222
173 18.750000
174 20.486111
175 6.944444
176 27.083333
177 2.083333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_hyper_percentages_per_day_array = \
CGMData_auto_daytime_hyper_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_hyper_percentages_per_day_arrayarray([10.76388889, 15.625 , 26.73611111, 13.54166667, 14.23611111,
23.95833333, 23.26388889, 11.80555556, 0.69444444, 39.93055556,
24.30555556, 18.40277778, 18.05555556, 18.05555556, 20.48611111,
31.94444444, 14.93055556, 11.11111111, 7.29166667, 32.98611111,
30.90277778, 8.68055556, 23.61111111, 15.625 , 12.5 ,
12.15277778, 13.19444444, 33.33333333, 17.36111111, 6.59722222,
17.70833333, 10.06944444, 5.90277778, 9.02777778, 21.18055556,
11.11111111, 29.16666667, 22.22222222, 12.5 , 4.51388889,
13.88888889, 20.48611111, 9.02777778, 7.63888889, 19.79166667,
16.31944444, 10.76388889, 16.31944444, 26.04166667, 34.72222222,
39.23611111, 26.04166667, 14.58333333, 20.13888889, 11.45833333,
35.41666667, 15.97222222, 24.30555556, 7.29166667, 22.56944444,
15.27777778, 15.97222222, 22.22222222, 19.79166667, 22.22222222,
17.70833333, 22.91666667, 18.40277778, 15.27777778, 0.69444444,
13.88888889, 20.83333333, 27.08333333, 19.09722222, 3.125 ,
7.63888889, 18.05555556, 29.86111111, 18.75 , 13.54166667,
14.58333333, 6.94444444, 10.41666667, 9.375 , 0.34722222,
20.13888889, 3.47222222, 12.5 , 2.43055556, 12.84722222,
23.61111111, 11.80555556, 19.79166667, 20.13888889, 2.43055556,
4.16666667, 6.94444444, 15.625 , 21.18055556, 24.30555556,
9.02777778, 14.93055556, 8.68055556, 6.94444444, 23.95833333,
35.76388889, 13.88888889, 23.61111111, 20.83333333, 20.83333333,
5.90277778, 0.34722222, 17.36111111, 17.36111111, 7.29166667,
6.59722222, 16.31944444, 7.98611111, 6.59722222, 2.08333333,
15.97222222, 20.83333333, 7.29166667, 15.27777778, 27.77777778,
13.88888889, 10.06944444, 13.54166667, 14.93055556, 15.27777778,
25. , 21.18055556, 16.66666667, 13.19444444, 13.19444444,
18.40277778, 15.625 , 11.11111111, 28.81944444, 17.70833333,
26.38888889, 24.30555556, 4.16666667, 13.88888889, 1.04166667,
25. , 6.25 , 20.83333333, 17.36111111, 17.01388889,
26.38888889, 9.02777778, 5.90277778, 20.13888889, 30.55555556,
20.48611111, 16.31944444, 10.76388889, 17.36111111, 14.23611111,
21.18055556, 14.58333333, 11.11111111, 19.79166667, 38.88888889,
23.61111111, 29.16666667, 17.70833333, 7.29166667, 15.625 ,
17.01388889, 37.15277778, 15.97222222, 18.75 , 20.48611111,
6.94444444, 27.08333333, 2.08333333])CGMData_auto_daytime_hyper_average_percentages_over_all_days = \
CGMData_auto_daytime_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_hyper_average_percentages_over_all_days.item()14.516625615763544Manual Mode / Daytime / Hyperglycemia Critical¶
CGMData_manual_daytime_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_manual_daytime_hyper_crit_df.describe()Loading...
CGMData_manual_daytime_hyper_crit_df[0:50]Loading...
CGMData_manual_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 26
1 48
2 11
3 4
4 7
5 5
6 5
7 51
8 71
9 58
10 53
11 41
12 44
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_hyper_crit_percentages_per_day_series = \
CGMData_manual_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_hyper_crit_percentages_per_day_series0 9.027778
1 16.666667
2 3.819444
3 1.388889
4 2.430556
5 1.736111
6 1.736111
7 17.708333
8 24.652778
9 20.138889
10 18.402778
11 14.236111
12 15.277778
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_hyper_crit_percentages_per_day_array = \
CGMData_manual_daytime_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_hyper_crit_percentages_per_day_arrayarray([ 9.02777778, 16.66666667, 3.81944444, 1.38888889, 2.43055556,
1.73611111, 1.73611111, 17.70833333, 24.65277778, 20.13888889,
18.40277778, 14.23611111, 15.27777778])CGMData_manual_daytime_hyper_crit_average_percentages_over_all_days = \
CGMData_manual_daytime_hyper_crit_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_daytime_hyper_crit_average_percentages_over_all_days.item()0.725232621784346Auto Mode / Daytime / Hyperglycemia Critical¶
CGMData_auto_daytime_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_auto_daytime_hyper_crit_df.describe()Loading...
CGMData_auto_daytime_hyper_crit_df[0:5]Loading...
CGMData_auto_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 62
1 37
2 12
3 25
4 1
5 2
6 24
7 31
8 43
9 46
10 42
11 9
12 13
13 5
14 72
15 41
16 21
17 30
18 20
19 9
20 18
21 56
22 11
23 3
24 43
25 11
26 7
27 35
28 14
29 29
30 11
31 21
32 18
33 4
34 26
35 18
36 9
37 47
38 14
39 13
40 10
41 29
42 30
43 27
44 26
45 14
46 17
47 2
48 45
49 29
50 12
51 44
52 8
53 29
54 24
55 7
56 1
57 34
58 4
59 16
60 35
61 16
62 40
63 8
64 9
65 36
66 6
67 25
68 21
69 26
70 2
71 23
72 2
73 21
74 12
75 28
76 10
77 8
78 13
79 18
80 21
81 7
82 8
83 7
84 20
85 30
86 25
87 6
88 13
89 12
90 4
91 39
92 11
93 20
94 9
95 17
96 50
97 30
98 35
99 3
100 11
101 5
102 10
103 33
104 20
105 16
106 36
107 16
108 47
109 2
110 20
111 15
112 15
113 3
114 8
115 27
116 24
117 10
118 23
119 10
120 9
121 20
122 26
123 52
124 1
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_hyper_crit_percentages_per_day_series = \
CGMData_auto_daytime_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_hyper_crit_percentages_per_day_series0 21.527778
1 12.847222
2 4.166667
3 8.680556
4 0.347222
5 0.694444
6 8.333333
7 10.763889
8 14.930556
9 15.972222
10 14.583333
11 3.125000
12 4.513889
13 1.736111
14 25.000000
15 14.236111
16 7.291667
17 10.416667
18 6.944444
19 3.125000
20 6.250000
21 19.444444
22 3.819444
23 1.041667
24 14.930556
25 3.819444
26 2.430556
27 12.152778
28 4.861111
29 10.069444
30 3.819444
31 7.291667
32 6.250000
33 1.388889
34 9.027778
35 6.250000
36 3.125000
37 16.319444
38 4.861111
39 4.513889
40 3.472222
41 10.069444
42 10.416667
43 9.375000
44 9.027778
45 4.861111
46 5.902778
47 0.694444
48 15.625000
49 10.069444
50 4.166667
51 15.277778
52 2.777778
53 10.069444
54 8.333333
55 2.430556
56 0.347222
57 11.805556
58 1.388889
59 5.555556
60 12.152778
61 5.555556
62 13.888889
63 2.777778
64 3.125000
65 12.500000
66 2.083333
67 8.680556
68 7.291667
69 9.027778
70 0.694444
71 7.986111
72 0.694444
73 7.291667
74 4.166667
75 9.722222
76 3.472222
77 2.777778
78 4.513889
79 6.250000
80 7.291667
81 2.430556
82 2.777778
83 2.430556
84 6.944444
85 10.416667
86 8.680556
87 2.083333
88 4.513889
89 4.166667
90 1.388889
91 13.541667
92 3.819444
93 6.944444
94 3.125000
95 5.902778
96 17.361111
97 10.416667
98 12.152778
99 1.041667
100 3.819444
101 1.736111
102 3.472222
103 11.458333
104 6.944444
105 5.555556
106 12.500000
107 5.555556
108 16.319444
109 0.694444
110 6.944444
111 5.208333
112 5.208333
113 1.041667
114 2.777778
115 9.375000
116 8.333333
117 3.472222
118 7.986111
119 3.472222
120 3.125000
121 6.944444
122 9.027778
123 18.055556
124 0.347222
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_hyper_crit_percentages_per_day_array = \
CGMData_auto_daytime_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_hyper_crit_percentages_per_day_arrayarray([21.52777778, 12.84722222, 4.16666667, 8.68055556, 0.34722222,
0.69444444, 8.33333333, 10.76388889, 14.93055556, 15.97222222,
14.58333333, 3.125 , 4.51388889, 1.73611111, 25. ,
14.23611111, 7.29166667, 10.41666667, 6.94444444, 3.125 ,
6.25 , 19.44444444, 3.81944444, 1.04166667, 14.93055556,
3.81944444, 2.43055556, 12.15277778, 4.86111111, 10.06944444,
3.81944444, 7.29166667, 6.25 , 1.38888889, 9.02777778,
6.25 , 3.125 , 16.31944444, 4.86111111, 4.51388889,
3.47222222, 10.06944444, 10.41666667, 9.375 , 9.02777778,
4.86111111, 5.90277778, 0.69444444, 15.625 , 10.06944444,
4.16666667, 15.27777778, 2.77777778, 10.06944444, 8.33333333,
2.43055556, 0.34722222, 11.80555556, 1.38888889, 5.55555556,
12.15277778, 5.55555556, 13.88888889, 2.77777778, 3.125 ,
12.5 , 2.08333333, 8.68055556, 7.29166667, 9.02777778,
0.69444444, 7.98611111, 0.69444444, 7.29166667, 4.16666667,
9.72222222, 3.47222222, 2.77777778, 4.51388889, 6.25 ,
7.29166667, 2.43055556, 2.77777778, 2.43055556, 6.94444444,
10.41666667, 8.68055556, 2.08333333, 4.51388889, 4.16666667,
1.38888889, 13.54166667, 3.81944444, 6.94444444, 3.125 ,
5.90277778, 17.36111111, 10.41666667, 12.15277778, 1.04166667,
3.81944444, 1.73611111, 3.47222222, 11.45833333, 6.94444444,
5.55555556, 12.5 , 5.55555556, 16.31944444, 0.69444444,
6.94444444, 5.20833333, 5.20833333, 1.04166667, 2.77777778,
9.375 , 8.33333333, 3.47222222, 7.98611111, 3.47222222,
3.125 , 6.94444444, 9.02777778, 18.05555556, 0.34722222])CGMData_auto_daytime_hyper_crit_average_percentages_over_all_days = \
CGMData_auto_daytime_hyper_crit_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_hyper_crit_average_percentages_over_all_days.item()4.354816639299398Manual Mode / Daytime / Normal¶
CGMData_manual_daytime_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_manual_daytime_normal_df.describe()Loading...
CGMData_manual_daytime_normal_df[0:50]Loading...
CGMData_manual_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 60
1 81
2 117
3 130
4 180
5 160
6 143
7 165
8 156
9 115
10 82
11 54
12 127
13 96
14 73
15 26
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_normal_percentages_per_day_series = \
CGMData_manual_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_normal_percentages_per_day_series0 20.833333
1 28.125000
2 40.625000
3 45.138889
4 62.500000
5 55.555556
6 49.652778
7 57.291667
8 54.166667
9 39.930556
10 28.472222
11 18.750000
12 44.097222
13 33.333333
14 25.347222
15 9.027778
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_normal_percentages_per_day_array = \
CGMData_manual_daytime_normal_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_normal_percentages_per_day_arrayarray([20.83333333, 28.125 , 40.625 , 45.13888889, 62.5 ,
55.55555556, 49.65277778, 57.29166667, 54.16666667, 39.93055556,
28.47222222, 18.75 , 44.09722222, 33.33333333, 25.34722222,
9.02777778])CGMData_manual_daytime_normal_average_percentages_over_all_days = \
CGMData_manual_daytime_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_daytime_normal_average_percentages_over_all_days.item()3.0189518336070056Auto Mode / Daytime / Normal¶
CGMData_auto_daytime_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_auto_daytime_normal_df.describe()Loading...
CGMData_auto_daytime_normal_df[0:5]Loading...
CGMData_auto_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 80
1 163
2 139
3 140
4 156
5 112
6 148
7 180
8 191
9 77
10 97
11 105
12 118
13 116
14 148
15 111
16 168
17 112
18 154
19 83
20 91
21 187
22 113
23 149
24 160
25 86
26 126
27 107
28 123
29 197
30 154
31 156
32 191
33 176
34 147
35 139
36 132
37 135
38 151
39 189
40 164
41 129
42 182
43 178
44 141
45 153
46 151
47 151
48 124
49 56
50 81
51 118
52 148
53 118
54 153
55 84
56 144
57 140
58 170
59 150
60 135
61 107
62 138
63 114
64 119
65 146
66 106
67 162
68 144
69 207
70 137
71 116
72 129
73 129
74 165
75 183
76 137
77 88
78 158
79 152
80 139
81 153
82 177
83 135
84 174
85 134
86 176
87 115
88 209
89 124
90 122
91 152
92 113
93 151
94 200
95 189
96 185
97 111
98 151
99 146
100 183
101 152
102 191
103 184
104 128
105 189
106 85
107 176
108 130
109 156
110 148
111 175
112 200
113 182
114 166
115 157
116 169
117 190
118 151
119 172
120 170
121 142
122 165
123 148
124 172
125 157
126 122
127 131
128 145
129 177
130 148
131 166
132 129
133 124
134 161
135 174
136 123
137 146
138 154
139 147
140 116
141 142
142 64
143 112
144 169
145 100
146 3
147 130
148 171
149 113
150 112
151 150
152 109
153 147
154 171
155 142
156 92
157 124
158 89
159 177
160 152
161 116
162 121
163 134
164 175
165 151
166 74
167 124
168 116
169 149
170 164
171 171
172 150
173 80
174 130
175 129
176 90
177 165
178 113
179 83
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_normal_percentages_per_day_series = \
CGMData_auto_daytime_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_normal_percentages_per_day_series0 27.777778
1 56.597222
2 48.263889
3 48.611111
4 54.166667
5 38.888889
6 51.388889
7 62.500000
8 66.319444
9 26.736111
10 33.680556
11 36.458333
12 40.972222
13 40.277778
14 51.388889
15 38.541667
16 58.333333
17 38.888889
18 53.472222
19 28.819444
20 31.597222
21 64.930556
22 39.236111
23 51.736111
24 55.555556
25 29.861111
26 43.750000
27 37.152778
28 42.708333
29 68.402778
30 53.472222
31 54.166667
32 66.319444
33 61.111111
34 51.041667
35 48.263889
36 45.833333
37 46.875000
38 52.430556
39 65.625000
40 56.944444
41 44.791667
42 63.194444
43 61.805556
44 48.958333
45 53.125000
46 52.430556
47 52.430556
48 43.055556
49 19.444444
50 28.125000
51 40.972222
52 51.388889
53 40.972222
54 53.125000
55 29.166667
56 50.000000
57 48.611111
58 59.027778
59 52.083333
60 46.875000
61 37.152778
62 47.916667
63 39.583333
64 41.319444
65 50.694444
66 36.805556
67 56.250000
68 50.000000
69 71.875000
70 47.569444
71 40.277778
72 44.791667
73 44.791667
74 57.291667
75 63.541667
76 47.569444
77 30.555556
78 54.861111
79 52.777778
80 48.263889
81 53.125000
82 61.458333
83 46.875000
84 60.416667
85 46.527778
86 61.111111
87 39.930556
88 72.569444
89 43.055556
90 42.361111
91 52.777778
92 39.236111
93 52.430556
94 69.444444
95 65.625000
96 64.236111
97 38.541667
98 52.430556
99 50.694444
100 63.541667
101 52.777778
102 66.319444
103 63.888889
104 44.444444
105 65.625000
106 29.513889
107 61.111111
108 45.138889
109 54.166667
110 51.388889
111 60.763889
112 69.444444
113 63.194444
114 57.638889
115 54.513889
116 58.680556
117 65.972222
118 52.430556
119 59.722222
120 59.027778
121 49.305556
122 57.291667
123 51.388889
124 59.722222
125 54.513889
126 42.361111
127 45.486111
128 50.347222
129 61.458333
130 51.388889
131 57.638889
132 44.791667
133 43.055556
134 55.902778
135 60.416667
136 42.708333
137 50.694444
138 53.472222
139 51.041667
140 40.277778
141 49.305556
142 22.222222
143 38.888889
144 58.680556
145 34.722222
146 1.041667
147 45.138889
148 59.375000
149 39.236111
150 38.888889
151 52.083333
152 37.847222
153 51.041667
154 59.375000
155 49.305556
156 31.944444
157 43.055556
158 30.902778
159 61.458333
160 52.777778
161 40.277778
162 42.013889
163 46.527778
164 60.763889
165 52.430556
166 25.694444
167 43.055556
168 40.277778
169 51.736111
170 56.944444
171 59.375000
172 52.083333
173 27.777778
174 45.138889
175 44.791667
176 31.250000
177 57.291667
178 39.236111
179 28.819444
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_normal_percentages_per_day_array = \
CGMData_auto_daytime_normal_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_normal_percentages_per_day_arrayarray([27.77777778, 56.59722222, 48.26388889, 48.61111111, 54.16666667,
38.88888889, 51.38888889, 62.5 , 66.31944444, 26.73611111,
33.68055556, 36.45833333, 40.97222222, 40.27777778, 51.38888889,
38.54166667, 58.33333333, 38.88888889, 53.47222222, 28.81944444,
31.59722222, 64.93055556, 39.23611111, 51.73611111, 55.55555556,
29.86111111, 43.75 , 37.15277778, 42.70833333, 68.40277778,
53.47222222, 54.16666667, 66.31944444, 61.11111111, 51.04166667,
48.26388889, 45.83333333, 46.875 , 52.43055556, 65.625 ,
56.94444444, 44.79166667, 63.19444444, 61.80555556, 48.95833333,
53.125 , 52.43055556, 52.43055556, 43.05555556, 19.44444444,
28.125 , 40.97222222, 51.38888889, 40.97222222, 53.125 ,
29.16666667, 50. , 48.61111111, 59.02777778, 52.08333333,
46.875 , 37.15277778, 47.91666667, 39.58333333, 41.31944444,
50.69444444, 36.80555556, 56.25 , 50. , 71.875 ,
47.56944444, 40.27777778, 44.79166667, 44.79166667, 57.29166667,
63.54166667, 47.56944444, 30.55555556, 54.86111111, 52.77777778,
48.26388889, 53.125 , 61.45833333, 46.875 , 60.41666667,
46.52777778, 61.11111111, 39.93055556, 72.56944444, 43.05555556,
42.36111111, 52.77777778, 39.23611111, 52.43055556, 69.44444444,
65.625 , 64.23611111, 38.54166667, 52.43055556, 50.69444444,
63.54166667, 52.77777778, 66.31944444, 63.88888889, 44.44444444,
65.625 , 29.51388889, 61.11111111, 45.13888889, 54.16666667,
51.38888889, 60.76388889, 69.44444444, 63.19444444, 57.63888889,
54.51388889, 58.68055556, 65.97222222, 52.43055556, 59.72222222,
59.02777778, 49.30555556, 57.29166667, 51.38888889, 59.72222222,
54.51388889, 42.36111111, 45.48611111, 50.34722222, 61.45833333,
51.38888889, 57.63888889, 44.79166667, 43.05555556, 55.90277778,
60.41666667, 42.70833333, 50.69444444, 53.47222222, 51.04166667,
40.27777778, 49.30555556, 22.22222222, 38.88888889, 58.68055556,
34.72222222, 1.04166667, 45.13888889, 59.375 , 39.23611111,
38.88888889, 52.08333333, 37.84722222, 51.04166667, 59.375 ,
49.30555556, 31.94444444, 43.05555556, 30.90277778, 61.45833333,
52.77777778, 40.27777778, 42.01388889, 46.52777778, 60.76388889,
52.43055556, 25.69444444, 43.05555556, 40.27777778, 51.73611111,
56.94444444, 59.375 , 52.08333333, 27.77777778, 45.13888889,
44.79166667, 31.25 , 57.29166667, 39.23611111, 28.81944444])CGMData_auto_daytime_normal_average_percentages_over_all_days = \
CGMData_auto_daytime_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_normal_average_percentages_over_all_days.item()43.402777777777786Manual Mode / Daytime / Secondary¶
CGMData_manual_daytime_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_manual_daytime_secondary_df.describe()Loading...
CGMData_manual_daytime_secondary_df[0:50]Loading...
CGMData_manual_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 41
1 71
2 70
3 116
4 147
5 136
6 138
7 110
8 120
9 100
10 62
11 46
12 108
13 55
14 42
15 18
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_secondary_percentages_per_day_series = \
CGMData_manual_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_secondary_percentages_per_day_series0 14.236111
1 24.652778
2 24.305556
3 40.277778
4 51.041667
5 47.222222
6 47.916667
7 38.194444
8 41.666667
9 34.722222
10 21.527778
11 15.972222
12 37.500000
13 19.097222
14 14.583333
15 6.250000
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_secondary_percentages_per_day_array = \
CGMData_manual_daytime_secondary_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_secondary_percentages_per_day_arrayarray([14.23611111, 24.65277778, 24.30555556, 40.27777778, 51.04166667,
47.22222222, 47.91666667, 38.19444444, 41.66666667, 34.72222222,
21.52777778, 15.97222222, 37.5 , 19.09722222, 14.58333333,
6.25 ])CGMData_manual_daytime_secondary_average_percentages_over_all_days = \
CGMData_manual_daytime_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_daytime_secondary_average_percentages_over_all_days.item()2.3604269293924465Auto Mode / Daytime / Secondary¶
CGMData_auto_daytime_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_auto_daytime_secondary_df.describe()Loading...
CGMData_auto_daytime_secondary_df[0:50]Loading...
CGMData_auto_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 60
1 120
2 98
3 115
4 110
5 88
6 110
7 166
8 145
9 35
10 57
11 92
12 36
13 80
14 100
15 83
16 146
17 106
18 130
19 52
20 58
21 121
22 83
23 100
24 150
25 72
26 121
27 82
28 91
29 148
30 126
31 115
32 108
33 120
34 106
35 107
36 76
37 84
38 136
39 142
40 115
41 56
42 124
43 146
44 57
45 122
46 128
47 107
48 94
49 13
50 57
51 56
52 111
53 85
54 104
55 60
56 112
57 96
58 149
59 136
60 110
61 81
62 84
63 66
64 89
65 107
66 68
67 118
68 121
69 197
70 95
71 61
72 98
73 115
74 159
75 102
76 103
77 49
78 114
79 112
80 122
81 130
82 104
83 97
84 164
85 104
86 152
87 103
88 180
89 99
90 85
91 141
92 99
93 117
94 181
95 142
96 141
97 104
98 117
99 72
100 159
101 119
102 153
103 127
104 85
105 169
106 57
107 131
108 106
109 93
110 82
111 129
112 179
113 154
114 128
115 115
116 104
117 144
118 126
119 144
120 110
121 137
122 143
123 131
124 133
125 120
126 66
127 92
128 119
129 122
130 138
131 125
132 114
133 113
134 74
135 127
136 102
137 89
138 99
139 124
140 81
141 113
142 58
143 70
144 125
145 81
146 77
147 116
148 70
149 102
150 119
151 85
152 123
153 110
154 114
155 45
156 77
157 77
158 148
159 86
160 92
161 89
162 120
163 142
164 93
165 25
166 82
167 86
168 91
169 133
170 138
171 101
172 39
173 95
174 93
175 70
176 138
177 62
178 52
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_secondary_percentages_per_day_series = \
CGMData_auto_daytime_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_secondary_percentages_per_day_series0 20.833333
1 41.666667
2 34.027778
3 39.930556
4 38.194444
5 30.555556
6 38.194444
7 57.638889
8 50.347222
9 12.152778
10 19.791667
11 31.944444
12 12.500000
13 27.777778
14 34.722222
15 28.819444
16 50.694444
17 36.805556
18 45.138889
19 18.055556
20 20.138889
21 42.013889
22 28.819444
23 34.722222
24 52.083333
25 25.000000
26 42.013889
27 28.472222
28 31.597222
29 51.388889
30 43.750000
31 39.930556
32 37.500000
33 41.666667
34 36.805556
35 37.152778
36 26.388889
37 29.166667
38 47.222222
39 49.305556
40 39.930556
41 19.444444
42 43.055556
43 50.694444
44 19.791667
45 42.361111
46 44.444444
47 37.152778
48 32.638889
49 4.513889
50 19.791667
51 19.444444
52 38.541667
53 29.513889
54 36.111111
55 20.833333
56 38.888889
57 33.333333
58 51.736111
59 47.222222
60 38.194444
61 28.125000
62 29.166667
63 22.916667
64 30.902778
65 37.152778
66 23.611111
67 40.972222
68 42.013889
69 68.402778
70 32.986111
71 21.180556
72 34.027778
73 39.930556
74 55.208333
75 35.416667
76 35.763889
77 17.013889
78 39.583333
79 38.888889
80 42.361111
81 45.138889
82 36.111111
83 33.680556
84 56.944444
85 36.111111
86 52.777778
87 35.763889
88 62.500000
89 34.375000
90 29.513889
91 48.958333
92 34.375000
93 40.625000
94 62.847222
95 49.305556
96 48.958333
97 36.111111
98 40.625000
99 25.000000
100 55.208333
101 41.319444
102 53.125000
103 44.097222
104 29.513889
105 58.680556
106 19.791667
107 45.486111
108 36.805556
109 32.291667
110 28.472222
111 44.791667
112 62.152778
113 53.472222
114 44.444444
115 39.930556
116 36.111111
117 50.000000
118 43.750000
119 50.000000
120 38.194444
121 47.569444
122 49.652778
123 45.486111
124 46.180556
125 41.666667
126 22.916667
127 31.944444
128 41.319444
129 42.361111
130 47.916667
131 43.402778
132 39.583333
133 39.236111
134 25.694444
135 44.097222
136 35.416667
137 30.902778
138 34.375000
139 43.055556
140 28.125000
141 39.236111
142 20.138889
143 24.305556
144 43.402778
145 28.125000
146 26.736111
147 40.277778
148 24.305556
149 35.416667
150 41.319444
151 29.513889
152 42.708333
153 38.194444
154 39.583333
155 15.625000
156 26.736111
157 26.736111
158 51.388889
159 29.861111
160 31.944444
161 30.902778
162 41.666667
163 49.305556
164 32.291667
165 8.680556
166 28.472222
167 29.861111
168 31.597222
169 46.180556
170 47.916667
171 35.069444
172 13.541667
173 32.986111
174 32.291667
175 24.305556
176 47.916667
177 21.527778
178 18.055556
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_secondary_percentages_per_day_array = \
CGMData_auto_daytime_secondary_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_secondary_percentages_per_day_arrayarray([20.83333333, 41.66666667, 34.02777778, 39.93055556, 38.19444444,
30.55555556, 38.19444444, 57.63888889, 50.34722222, 12.15277778,
19.79166667, 31.94444444, 12.5 , 27.77777778, 34.72222222,
28.81944444, 50.69444444, 36.80555556, 45.13888889, 18.05555556,
20.13888889, 42.01388889, 28.81944444, 34.72222222, 52.08333333,
25. , 42.01388889, 28.47222222, 31.59722222, 51.38888889,
43.75 , 39.93055556, 37.5 , 41.66666667, 36.80555556,
37.15277778, 26.38888889, 29.16666667, 47.22222222, 49.30555556,
39.93055556, 19.44444444, 43.05555556, 50.69444444, 19.79166667,
42.36111111, 44.44444444, 37.15277778, 32.63888889, 4.51388889,
19.79166667, 19.44444444, 38.54166667, 29.51388889, 36.11111111,
20.83333333, 38.88888889, 33.33333333, 51.73611111, 47.22222222,
38.19444444, 28.125 , 29.16666667, 22.91666667, 30.90277778,
37.15277778, 23.61111111, 40.97222222, 42.01388889, 68.40277778,
32.98611111, 21.18055556, 34.02777778, 39.93055556, 55.20833333,
35.41666667, 35.76388889, 17.01388889, 39.58333333, 38.88888889,
42.36111111, 45.13888889, 36.11111111, 33.68055556, 56.94444444,
36.11111111, 52.77777778, 35.76388889, 62.5 , 34.375 ,
29.51388889, 48.95833333, 34.375 , 40.625 , 62.84722222,
49.30555556, 48.95833333, 36.11111111, 40.625 , 25. ,
55.20833333, 41.31944444, 53.125 , 44.09722222, 29.51388889,
58.68055556, 19.79166667, 45.48611111, 36.80555556, 32.29166667,
28.47222222, 44.79166667, 62.15277778, 53.47222222, 44.44444444,
39.93055556, 36.11111111, 50. , 43.75 , 50. ,
38.19444444, 47.56944444, 49.65277778, 45.48611111, 46.18055556,
41.66666667, 22.91666667, 31.94444444, 41.31944444, 42.36111111,
47.91666667, 43.40277778, 39.58333333, 39.23611111, 25.69444444,
44.09722222, 35.41666667, 30.90277778, 34.375 , 43.05555556,
28.125 , 39.23611111, 20.13888889, 24.30555556, 43.40277778,
28.125 , 26.73611111, 40.27777778, 24.30555556, 35.41666667,
41.31944444, 29.51388889, 42.70833333, 38.19444444, 39.58333333,
15.625 , 26.73611111, 26.73611111, 51.38888889, 29.86111111,
31.94444444, 30.90277778, 41.66666667, 49.30555556, 32.29166667,
8.68055556, 28.47222222, 29.86111111, 31.59722222, 46.18055556,
47.91666667, 35.06944444, 13.54166667, 32.98611111, 32.29166667,
24.30555556, 47.91666667, 21.52777778, 18.05555556])CGMData_auto_daytime_secondary_average_percentages_over_all_days = \
CGMData_auto_daytime_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_secondary_average_percentages_over_all_days.item()32.337848932676515Manual Mode / Daytime / Hypoglycemia Level 1¶
CGMData_manual_daytime_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_manual_daytime_hypo_1_df.describe()Loading...
CGMData_manual_daytime_hypo_1_df[0:50]Loading...
CGMData_manual_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 32
1 9
2 6
3 15
4 9
5 16
6 11
7 9
8 9
9 16
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_hypo_1_percentages_per_day_series = \
CGMData_manual_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_hypo_1_percentages_per_day_series0 11.111111
1 3.125000
2 2.083333
3 5.208333
4 3.125000
5 5.555556
6 3.819444
7 3.125000
8 3.125000
9 5.555556
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_hypo_1_percentages_per_day_array = \
CGMData_manual_daytime_hypo_1_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_hypo_1_percentages_per_day_arrayarray([11.11111111, 3.125 , 2.08333333, 5.20833333, 3.125 ,
5.55555556, 3.81944444, 3.125 , 3.125 , 5.55555556])CGMData_manual_daytime_hypo_1_average_percentages_over_all_days = \
CGMData_manual_daytime_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_daytime_hypo_1_average_percentages_over_all_days.item()0.2257799671592775Auto Mode / Daytime / Hypoglycemia Level 1¶
CGMData_auto_daytime_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_auto_daytime_hypo_1_df.describe()Loading...
CGMData_auto_daytime_hypo_1_df[0:5]Loading...
CGMData_auto_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 17
1 4
2 2
3 6
4 14
5 3
6 10
7 4
8 10
9 7
10 2
11 36
12 37
13 3
14 15
15 8
16 7
17 10
18 3
19 3
20 5
21 7
22 14
23 12
24 5
25 2
26 5
27 3
28 8
29 7
30 3
31 5
32 11
33 1
34 12
35 54
36 11
37 4
38 7
39 9
40 5
41 7
42 2
43 21
44 30
45 7
46 22
47 9
48 10
49 2
50 8
51 39
52 8
53 11
54 9
55 13
56 1
57 9
58 10
59 7
60 7
61 5
62 21
63 3
64 4
65 5
66 5
67 12
68 8
69 3
70 8
71 19
72 2
73 14
74 7
75 9
76 34
77 2
78 8
79 10
80 13
81 6
82 11
83 1
84 2
85 18
86 16
87 11
88 6
89 17
90 2
91 2
92 17
93 4
94 12
95 9
96 18
97 24
98 9
99 5
100 19
101 43
102 8
103 11
104 5
105 3
106 2
107 15
108 6
109 11
110 6
111 16
112 8
113 5
114 6
115 11
116 6
117 10
118 30
119 24
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_hypo_1_percentages_per_day_series = \
CGMData_auto_daytime_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_hypo_1_percentages_per_day_series0 5.902778
1 1.388889
2 0.694444
3 2.083333
4 4.861111
5 1.041667
6 3.472222
7 1.388889
8 3.472222
9 2.430556
10 0.694444
11 12.500000
12 12.847222
13 1.041667
14 5.208333
15 2.777778
16 2.430556
17 3.472222
18 1.041667
19 1.041667
20 1.736111
21 2.430556
22 4.861111
23 4.166667
24 1.736111
25 0.694444
26 1.736111
27 1.041667
28 2.777778
29 2.430556
30 1.041667
31 1.736111
32 3.819444
33 0.347222
34 4.166667
35 18.750000
36 3.819444
37 1.388889
38 2.430556
39 3.125000
40 1.736111
41 2.430556
42 0.694444
43 7.291667
44 10.416667
45 2.430556
46 7.638889
47 3.125000
48 3.472222
49 0.694444
50 2.777778
51 13.541667
52 2.777778
53 3.819444
54 3.125000
55 4.513889
56 0.347222
57 3.125000
58 3.472222
59 2.430556
60 2.430556
61 1.736111
62 7.291667
63 1.041667
64 1.388889
65 1.736111
66 1.736111
67 4.166667
68 2.777778
69 1.041667
70 2.777778
71 6.597222
72 0.694444
73 4.861111
74 2.430556
75 3.125000
76 11.805556
77 0.694444
78 2.777778
79 3.472222
80 4.513889
81 2.083333
82 3.819444
83 0.347222
84 0.694444
85 6.250000
86 5.555556
87 3.819444
88 2.083333
89 5.902778
90 0.694444
91 0.694444
92 5.902778
93 1.388889
94 4.166667
95 3.125000
96 6.250000
97 8.333333
98 3.125000
99 1.736111
100 6.597222
101 14.930556
102 2.777778
103 3.819444
104 1.736111
105 1.041667
106 0.694444
107 5.208333
108 2.083333
109 3.819444
110 2.083333
111 5.555556
112 2.777778
113 1.736111
114 2.083333
115 3.819444
116 2.083333
117 3.472222
118 10.416667
119 8.333333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_hypo_1_percentages_per_day_array = \
CGMData_auto_daytime_hypo_1_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_hypo_1_percentages_per_day_arrayarray([ 5.90277778, 1.38888889, 0.69444444, 2.08333333, 4.86111111,
1.04166667, 3.47222222, 1.38888889, 3.47222222, 2.43055556,
0.69444444, 12.5 , 12.84722222, 1.04166667, 5.20833333,
2.77777778, 2.43055556, 3.47222222, 1.04166667, 1.04166667,
1.73611111, 2.43055556, 4.86111111, 4.16666667, 1.73611111,
0.69444444, 1.73611111, 1.04166667, 2.77777778, 2.43055556,
1.04166667, 1.73611111, 3.81944444, 0.34722222, 4.16666667,
18.75 , 3.81944444, 1.38888889, 2.43055556, 3.125 ,
1.73611111, 2.43055556, 0.69444444, 7.29166667, 10.41666667,
2.43055556, 7.63888889, 3.125 , 3.47222222, 0.69444444,
2.77777778, 13.54166667, 2.77777778, 3.81944444, 3.125 ,
4.51388889, 0.34722222, 3.125 , 3.47222222, 2.43055556,
2.43055556, 1.73611111, 7.29166667, 1.04166667, 1.38888889,
1.73611111, 1.73611111, 4.16666667, 2.77777778, 1.04166667,
2.77777778, 6.59722222, 0.69444444, 4.86111111, 2.43055556,
3.125 , 11.80555556, 0.69444444, 2.77777778, 3.47222222,
4.51388889, 2.08333333, 3.81944444, 0.34722222, 0.69444444,
6.25 , 5.55555556, 3.81944444, 2.08333333, 5.90277778,
0.69444444, 0.69444444, 5.90277778, 1.38888889, 4.16666667,
3.125 , 6.25 , 8.33333333, 3.125 , 1.73611111,
6.59722222, 14.93055556, 2.77777778, 3.81944444, 1.73611111,
1.04166667, 0.69444444, 5.20833333, 2.08333333, 3.81944444,
2.08333333, 5.55555556, 2.77777778, 1.73611111, 2.08333333,
3.81944444, 2.08333333, 3.47222222, 10.41666667, 8.33333333])CGMData_auto_daytime_hypo_1_average_percentages_over_all_days = \
CGMData_auto_daytime_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_hypo_1_average_percentages_over_all_days.item()2.1380678708264913Manual Mode / Daytime / Hypoglycemia Level 2¶
CGMData_manual_daytime_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_manual_daytime_hypo_2_df.describe()Loading...
CGMData_manual_daytime_hypo_2_df[0:50]Loading...
CGMData_manual_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 13
1 19
2 9
3 14
4 8
5 2
6 7
7 12
8 4
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_daytime_hypo_2_percentages_per_day_series = \
CGMData_manual_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_daytime_hypo_2_percentages_per_day_series0 4.513889
1 6.597222
2 3.125000
3 4.861111
4 2.777778
5 0.694444
6 2.430556
7 4.166667
8 1.388889
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_daytime_hypo_2_percentages_per_day_array = \
CGMData_manual_daytime_hypo_2_percentages_per_day_series.to_numpy()
CGMData_manual_daytime_hypo_2_percentages_per_day_arrayarray([4.51388889, 6.59722222, 3.125 , 4.86111111, 2.77777778,
0.69444444, 2.43055556, 4.16666667, 1.38888889])CGMData_manual_daytime_hypo_2_average_percentages_over_all_days = \
CGMData_manual_daytime_hypo_2_percentages_per_day_series.sum() / number_of_days_in_data
CGMData_manual_daytime_hypo_2_average_percentages_over_all_days.item()0.150519978106185Auto Mode / Daytime / Hypoglycemia Level 2¶
CGMData_auto_daytime_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_auto_daytime_hypo_2_df.describe()Loading...
CGMData_auto_daytime_hypo_2_df[0:5]Loading...
CGMData_auto_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 3
1 8
2 10
3 4
4 12
5 7
6 5
7 6
8 3
9 4
10 1
11 16
12 7
13 1
14 2
15 3
16 11
17 3
18 7
19 8
20 1
21 1
22 8
23 6
24 3
25 11
26 5
27 9
28 7
29 4
30 12
31 3
32 4
33 17
34 7
35 6
36 1
37 10
38 20
39 28
40 10
41 2
42 10
43 16
44 1
45 2
46 14
47 8
48 12
49 7
50 12
51 11
52 27
53 5
54 2
55 14
56 6
57 1
58 6
59 4
60 9
61 9
62 9
63 12
64 12
65 5
66 6
67 3
68 5
69 9
70 24
71 13
72 14
73 2
74 6
75 9
76 1
77 5
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_daytime_hypo_2_percentages_per_day_series = \
CGMData_auto_daytime_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_daytime_hypo_2_percentages_per_day_series0 1.041667
1 2.777778
2 3.472222
3 1.388889
4 4.166667
5 2.430556
6 1.736111
7 2.083333
8 1.041667
9 1.388889
10 0.347222
11 5.555556
12 2.430556
13 0.347222
14 0.694444
15 1.041667
16 3.819444
17 1.041667
18 2.430556
19 2.777778
20 0.347222
21 0.347222
22 2.777778
23 2.083333
24 1.041667
25 3.819444
26 1.736111
27 3.125000
28 2.430556
29 1.388889
30 4.166667
31 1.041667
32 1.388889
33 5.902778
34 2.430556
35 2.083333
36 0.347222
37 3.472222
38 6.944444
39 9.722222
40 3.472222
41 0.694444
42 3.472222
43 5.555556
44 0.347222
45 0.694444
46 4.861111
47 2.777778
48 4.166667
49 2.430556
50 4.166667
51 3.819444
52 9.375000
53 1.736111
54 0.694444
55 4.861111
56 2.083333
57 0.347222
58 2.083333
59 1.388889
60 3.125000
61 3.125000
62 3.125000
63 4.166667
64 4.166667
65 1.736111
66 2.083333
67 1.041667
68 1.736111
69 3.125000
70 8.333333
71 4.513889
72 4.861111
73 0.694444
74 2.083333
75 3.125000
76 0.347222
77 1.736111
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_daytime_hypo_2_percentages_per_day_array = \
CGMData_auto_daytime_hypo_2_percentages_per_day_series.to_numpy()
CGMData_auto_daytime_hypo_2_percentages_per_day_arrayarray([1.04166667, 2.77777778, 3.47222222, 1.38888889, 4.16666667,
2.43055556, 1.73611111, 2.08333333, 1.04166667, 1.38888889,
0.34722222, 5.55555556, 2.43055556, 0.34722222, 0.69444444,
1.04166667, 3.81944444, 1.04166667, 2.43055556, 2.77777778,
0.34722222, 0.34722222, 2.77777778, 2.08333333, 1.04166667,
3.81944444, 1.73611111, 3.125 , 2.43055556, 1.38888889,
4.16666667, 1.04166667, 1.38888889, 5.90277778, 2.43055556,
2.08333333, 0.34722222, 3.47222222, 6.94444444, 9.72222222,
3.47222222, 0.69444444, 3.47222222, 5.55555556, 0.34722222,
0.69444444, 4.86111111, 2.77777778, 4.16666667, 2.43055556,
4.16666667, 3.81944444, 9.375 , 1.73611111, 0.69444444,
4.86111111, 2.08333333, 0.34722222, 2.08333333, 1.38888889,
3.125 , 3.125 , 3.125 , 4.16666667, 4.16666667,
1.73611111, 2.08333333, 1.04166667, 1.73611111, 3.125 ,
8.33333333, 4.51388889, 4.86111111, 0.69444444, 2.08333333,
3.125 , 0.34722222, 1.73611111])CGMData_auto_daytime_hypo_2_average_percentages_over_all_days = \
CGMData_auto_daytime_hypo_2_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_daytime_hypo_2_average_percentages_over_all_days.item()1.0382457580733442Manual Mode / Whole Day / Hyperglycemia¶
CGMData_manual_wholeday_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_manual_wholeday_hyper_df.describe()Loading...
CGMData_manual_wholeday_hyper_df[0:5]Loading...
CGMData_manual_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 12
1 59
2 119
3 53
4 59
5 34
6 59
7 63
8 49
9 60
10 63
11 80
12 36
13 59
14 96
15 32
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_hyper_percentages_per_day_series = \
CGMData_manual_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_hyper_percentages_per_day_series0 4.166667
1 20.486111
2 41.319444
3 18.402778
4 20.486111
5 11.805556
6 20.486111
7 21.875000
8 17.013889
9 20.833333
10 21.875000
11 27.777778
12 12.500000
13 20.486111
14 33.333333
15 11.111111
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_hyper_percentages_per_day_array = \
CGMData_manual_wholeday_hyper_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_hyper_percentages_per_day_arrayarray([ 4.16666667, 20.48611111, 41.31944444, 18.40277778, 20.48611111,
11.80555556, 20.48611111, 21.875 , 17.01388889, 20.83333333,
21.875 , 27.77777778, 12.5 , 20.48611111, 33.33333333,
11.11111111])CGMData_manual_wholeday_hyper_average_percentages_over_all_days = \
CGMData_manual_wholeday_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_wholeday_hyper_average_percentages_over_all_days.item()1.5958538587848932Auto Mode / Whole Day / Hyperglycemia¶
CGMData_auto_wholeday_hyper_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia')
)]
CGMData_auto_wholeday_hyper_df.describe()Loading...
CGMData_auto_wholeday_hyper_df[0:5]Loading...
CGMData_auto_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 31
1 84
2 77
3 39
4 41
5 69
6 67
7 34
8 2
9 115
10 85
11 53
12 69
13 52
14 63
15 137
16 43
17 32
18 21
19 113
20 126
21 30
22 68
23 47
24 36
25 35
26 51
27 97
28 57
29 19
30 51
31 29
32 17
33 26
34 61
35 37
36 107
37 64
38 36
39 13
40 40
41 59
42 26
43 22
44 57
45 47
46 49
47 47
48 75
49 116
50 113
51 76
52 94
53 120
54 33
55 102
56 46
57 89
58 24
59 65
60 102
61 46
62 64
63 57
64 64
65 51
66 66
67 53
68 44
69 2
70 40
71 60
72 78
73 63
74 9
75 22
76 54
77 86
78 60
79 39
80 56
81 38
82 30
83 28
84 1
85 58
86 10
87 36
88 17
89 38
90 72
91 55
92 67
93 69
94 7
95 12
96 44
97 45
98 61
99 75
100 35
101 43
102 25
103 20
104 69
105 9
106 117
107 79
108 68
109 60
110 60
111 17
112 19
113 50
114 50
115 21
116 19
117 47
118 23
119 60
120 14
121 46
122 92
123 37
124 44
125 85
126 40
127 32
128 39
129 45
130 48
131 85
132 66
133 48
134 55
135 50
136 62
137 45
138 32
139 107
140 59
141 88
142 70
143 26
144 40
145 3
146 77
147 45
148 80
149 50
150 56
151 111
152 50
153 25
154 58
155 88
156 67
157 47
158 37
159 52
160 54
161 61
162 64
163 32
164 65
165 112
166 75
167 115
168 77
169 59
170 69
171 49
172 129
173 50
174 56
175 63
176 33
177 78
178 13
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_hyper_percentages_per_day_series = \
CGMData_auto_wholeday_hyper_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_hyper_percentages_per_day_series0 10.763889
1 29.166667
2 26.736111
3 13.541667
4 14.236111
5 23.958333
6 23.263889
7 11.805556
8 0.694444
9 39.930556
10 29.513889
11 18.402778
12 23.958333
13 18.055556
14 21.875000
15 47.569444
16 14.930556
17 11.111111
18 7.291667
19 39.236111
20 43.750000
21 10.416667
22 23.611111
23 16.319444
24 12.500000
25 12.152778
26 17.708333
27 33.680556
28 19.791667
29 6.597222
30 17.708333
31 10.069444
32 5.902778
33 9.027778
34 21.180556
35 12.847222
36 37.152778
37 22.222222
38 12.500000
39 4.513889
40 13.888889
41 20.486111
42 9.027778
43 7.638889
44 19.791667
45 16.319444
46 17.013889
47 16.319444
48 26.041667
49 40.277778
50 39.236111
51 26.388889
52 32.638889
53 41.666667
54 11.458333
55 35.416667
56 15.972222
57 30.902778
58 8.333333
59 22.569444
60 35.416667
61 15.972222
62 22.222222
63 19.791667
64 22.222222
65 17.708333
66 22.916667
67 18.402778
68 15.277778
69 0.694444
70 13.888889
71 20.833333
72 27.083333
73 21.875000
74 3.125000
75 7.638889
76 18.750000
77 29.861111
78 20.833333
79 13.541667
80 19.444444
81 13.194444
82 10.416667
83 9.722222
84 0.347222
85 20.138889
86 3.472222
87 12.500000
88 5.902778
89 13.194444
90 25.000000
91 19.097222
92 23.263889
93 23.958333
94 2.430556
95 4.166667
96 15.277778
97 15.625000
98 21.180556
99 26.041667
100 12.152778
101 14.930556
102 8.680556
103 6.944444
104 23.958333
105 3.125000
106 40.625000
107 27.430556
108 23.611111
109 20.833333
110 20.833333
111 5.902778
112 6.597222
113 17.361111
114 17.361111
115 7.291667
116 6.597222
117 16.319444
118 7.986111
119 20.833333
120 4.861111
121 15.972222
122 31.944444
123 12.847222
124 15.277778
125 29.513889
126 13.888889
127 11.111111
128 13.541667
129 15.625000
130 16.666667
131 29.513889
132 22.916667
133 16.666667
134 19.097222
135 17.361111
136 21.527778
137 15.625000
138 11.111111
139 37.152778
140 20.486111
141 30.555556
142 24.305556
143 9.027778
144 13.888889
145 1.041667
146 26.736111
147 15.625000
148 27.777778
149 17.361111
150 19.444444
151 38.541667
152 17.361111
153 8.680556
154 20.138889
155 30.555556
156 23.263889
157 16.319444
158 12.847222
159 18.055556
160 18.750000
161 21.180556
162 22.222222
163 11.111111
164 22.569444
165 38.888889
166 26.041667
167 39.930556
168 26.736111
169 20.486111
170 23.958333
171 17.013889
172 44.791667
173 17.361111
174 19.444444
175 21.875000
176 11.458333
177 27.083333
178 4.513889
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_hyper_percentages_per_day_array = \
CGMData_auto_wholeday_hyper_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_hyper_percentages_per_day_arrayarray([10.76388889, 29.16666667, 26.73611111, 13.54166667, 14.23611111,
23.95833333, 23.26388889, 11.80555556, 0.69444444, 39.93055556,
29.51388889, 18.40277778, 23.95833333, 18.05555556, 21.875 ,
47.56944444, 14.93055556, 11.11111111, 7.29166667, 39.23611111,
43.75 , 10.41666667, 23.61111111, 16.31944444, 12.5 ,
12.15277778, 17.70833333, 33.68055556, 19.79166667, 6.59722222,
17.70833333, 10.06944444, 5.90277778, 9.02777778, 21.18055556,
12.84722222, 37.15277778, 22.22222222, 12.5 , 4.51388889,
13.88888889, 20.48611111, 9.02777778, 7.63888889, 19.79166667,
16.31944444, 17.01388889, 16.31944444, 26.04166667, 40.27777778,
39.23611111, 26.38888889, 32.63888889, 41.66666667, 11.45833333,
35.41666667, 15.97222222, 30.90277778, 8.33333333, 22.56944444,
35.41666667, 15.97222222, 22.22222222, 19.79166667, 22.22222222,
17.70833333, 22.91666667, 18.40277778, 15.27777778, 0.69444444,
13.88888889, 20.83333333, 27.08333333, 21.875 , 3.125 ,
7.63888889, 18.75 , 29.86111111, 20.83333333, 13.54166667,
19.44444444, 13.19444444, 10.41666667, 9.72222222, 0.34722222,
20.13888889, 3.47222222, 12.5 , 5.90277778, 13.19444444,
25. , 19.09722222, 23.26388889, 23.95833333, 2.43055556,
4.16666667, 15.27777778, 15.625 , 21.18055556, 26.04166667,
12.15277778, 14.93055556, 8.68055556, 6.94444444, 23.95833333,
3.125 , 40.625 , 27.43055556, 23.61111111, 20.83333333,
20.83333333, 5.90277778, 6.59722222, 17.36111111, 17.36111111,
7.29166667, 6.59722222, 16.31944444, 7.98611111, 20.83333333,
4.86111111, 15.97222222, 31.94444444, 12.84722222, 15.27777778,
29.51388889, 13.88888889, 11.11111111, 13.54166667, 15.625 ,
16.66666667, 29.51388889, 22.91666667, 16.66666667, 19.09722222,
17.36111111, 21.52777778, 15.625 , 11.11111111, 37.15277778,
20.48611111, 30.55555556, 24.30555556, 9.02777778, 13.88888889,
1.04166667, 26.73611111, 15.625 , 27.77777778, 17.36111111,
19.44444444, 38.54166667, 17.36111111, 8.68055556, 20.13888889,
30.55555556, 23.26388889, 16.31944444, 12.84722222, 18.05555556,
18.75 , 21.18055556, 22.22222222, 11.11111111, 22.56944444,
38.88888889, 26.04166667, 39.93055556, 26.73611111, 20.48611111,
23.95833333, 17.01388889, 44.79166667, 17.36111111, 19.44444444,
21.875 , 11.45833333, 27.08333333, 4.51388889])CGMData_auto_wholeday_hyper_average_percentages_over_all_days = \
CGMData_auto_wholeday_hyper_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_hyper_average_percentages_over_all_days.item()16.692323481116585Manual Mode / Whole Day / Hyperglycemia Critical¶
CGMData_manual_wholeday_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_manual_wholeday_hyper_crit_df.describe()Loading...
CGMData_manual_wholeday_hyper_crit_df[0:50]Loading...
CGMData_manual_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 26
1 48
2 11
3 4
4 7
5 5
6 21
7 80
8 71
9 58
10 53
11 41
12 44
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_hyper_crit_percentages_per_day_series = \
CGMData_manual_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_hyper_crit_percentages_per_day_series0 9.027778
1 16.666667
2 3.819444
3 1.388889
4 2.430556
5 1.736111
6 7.291667
7 27.777778
8 24.652778
9 20.138889
10 18.402778
11 14.236111
12 15.277778
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_hyper_crit_percentages_per_day_array = \
CGMData_manual_wholeday_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_hyper_crit_percentages_per_day_arrayarray([ 9.02777778, 16.66666667, 3.81944444, 1.38888889, 2.43055556,
1.73611111, 7.29166667, 27.77777778, 24.65277778, 20.13888889,
18.40277778, 14.23611111, 15.27777778])CGMData_manual_wholeday_hyper_crit_average_percentages_over_all_days = \
CGMData_manual_wholeday_hyper_crit_percentages_per_day_series.sum() / number_of_days_in_data
CGMData_manual_wholeday_hyper_crit_average_percentages_over_all_days.item()0.8022030651340996Auto Mode / Whole Day / Hyperglycemia Critical¶
CGMData_auto_wholeday_hyper_crit_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hyperglycemia critical')
)]
CGMData_auto_wholeday_hyper_crit_df.describe()Loading...
CGMData_auto_wholeday_hyper_crit_df[0:5]Loading...
CGMData_auto_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 62
1 12
2 37
3 12
4 25
5 1
6 2
7 24
8 31
9 43
10 71
11 42
12 9
13 13
14 5
15 72
16 41
17 21
18 31
19 20
20 9
21 18
22 56
23 42
24 3
25 43
26 11
27 7
28 35
29 14
30 29
31 11
32 21
33 18
34 4
35 32
36 18
37 9
38 54
39 14
40 13
41 10
42 31
43 30
44 27
45 26
46 14
47 17
48 2
49 45
50 29
51 12
52 44
53 8
54 29
55 24
56 7
57 1
58 34
59 11
60 16
61 49
62 17
63 40
64 8
65 9
66 36
67 6
68 25
69 21
70 29
71 6
72 6
73 23
74 2
75 21
76 12
77 28
78 10
79 8
80 13
81 18
82 21
83 2
84 7
85 8
86 7
87 20
88 30
89 25
90 6
91 13
92 12
93 4
94 39
95 11
96 11
97 20
98 9
99 22
100 57
101 30
102 63
103 3
104 11
105 5
106 8
107 10
108 33
109 3
110 20
111 16
112 36
113 16
114 47
115 11
116 2
117 20
118 15
119 15
120 3
121 8
122 27
123 26
124 10
125 23
126 23
127 10
128 9
129 22
130 32
131 52
132 1
133 10
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_hyper_crit_percentages_per_day_series = \
CGMData_auto_wholeday_hyper_crit_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_hyper_crit_percentages_per_day_series0 21.527778
1 4.166667
2 12.847222
3 4.166667
4 8.680556
5 0.347222
6 0.694444
7 8.333333
8 10.763889
9 14.930556
10 24.652778
11 14.583333
12 3.125000
13 4.513889
14 1.736111
15 25.000000
16 14.236111
17 7.291667
18 10.763889
19 6.944444
20 3.125000
21 6.250000
22 19.444444
23 14.583333
24 1.041667
25 14.930556
26 3.819444
27 2.430556
28 12.152778
29 4.861111
30 10.069444
31 3.819444
32 7.291667
33 6.250000
34 1.388889
35 11.111111
36 6.250000
37 3.125000
38 18.750000
39 4.861111
40 4.513889
41 3.472222
42 10.763889
43 10.416667
44 9.375000
45 9.027778
46 4.861111
47 5.902778
48 0.694444
49 15.625000
50 10.069444
51 4.166667
52 15.277778
53 2.777778
54 10.069444
55 8.333333
56 2.430556
57 0.347222
58 11.805556
59 3.819444
60 5.555556
61 17.013889
62 5.902778
63 13.888889
64 2.777778
65 3.125000
66 12.500000
67 2.083333
68 8.680556
69 7.291667
70 10.069444
71 2.083333
72 2.083333
73 7.986111
74 0.694444
75 7.291667
76 4.166667
77 9.722222
78 3.472222
79 2.777778
80 4.513889
81 6.250000
82 7.291667
83 0.694444
84 2.430556
85 2.777778
86 2.430556
87 6.944444
88 10.416667
89 8.680556
90 2.083333
91 4.513889
92 4.166667
93 1.388889
94 13.541667
95 3.819444
96 3.819444
97 6.944444
98 3.125000
99 7.638889
100 19.791667
101 10.416667
102 21.875000
103 1.041667
104 3.819444
105 1.736111
106 2.777778
107 3.472222
108 11.458333
109 1.041667
110 6.944444
111 5.555556
112 12.500000
113 5.555556
114 16.319444
115 3.819444
116 0.694444
117 6.944444
118 5.208333
119 5.208333
120 1.041667
121 2.777778
122 9.375000
123 9.027778
124 3.472222
125 7.986111
126 7.986111
127 3.472222
128 3.125000
129 7.638889
130 11.111111
131 18.055556
132 0.347222
133 3.472222
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_hyper_crit_percentages_per_day_array = \
CGMData_auto_wholeday_hyper_crit_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_hyper_crit_percentages_per_day_arrayarray([21.52777778, 4.16666667, 12.84722222, 4.16666667, 8.68055556,
0.34722222, 0.69444444, 8.33333333, 10.76388889, 14.93055556,
24.65277778, 14.58333333, 3.125 , 4.51388889, 1.73611111,
25. , 14.23611111, 7.29166667, 10.76388889, 6.94444444,
3.125 , 6.25 , 19.44444444, 14.58333333, 1.04166667,
14.93055556, 3.81944444, 2.43055556, 12.15277778, 4.86111111,
10.06944444, 3.81944444, 7.29166667, 6.25 , 1.38888889,
11.11111111, 6.25 , 3.125 , 18.75 , 4.86111111,
4.51388889, 3.47222222, 10.76388889, 10.41666667, 9.375 ,
9.02777778, 4.86111111, 5.90277778, 0.69444444, 15.625 ,
10.06944444, 4.16666667, 15.27777778, 2.77777778, 10.06944444,
8.33333333, 2.43055556, 0.34722222, 11.80555556, 3.81944444,
5.55555556, 17.01388889, 5.90277778, 13.88888889, 2.77777778,
3.125 , 12.5 , 2.08333333, 8.68055556, 7.29166667,
10.06944444, 2.08333333, 2.08333333, 7.98611111, 0.69444444,
7.29166667, 4.16666667, 9.72222222, 3.47222222, 2.77777778,
4.51388889, 6.25 , 7.29166667, 0.69444444, 2.43055556,
2.77777778, 2.43055556, 6.94444444, 10.41666667, 8.68055556,
2.08333333, 4.51388889, 4.16666667, 1.38888889, 13.54166667,
3.81944444, 3.81944444, 6.94444444, 3.125 , 7.63888889,
19.79166667, 10.41666667, 21.875 , 1.04166667, 3.81944444,
1.73611111, 2.77777778, 3.47222222, 11.45833333, 1.04166667,
6.94444444, 5.55555556, 12.5 , 5.55555556, 16.31944444,
3.81944444, 0.69444444, 6.94444444, 5.20833333, 5.20833333,
1.04166667, 2.77777778, 9.375 , 9.02777778, 3.47222222,
7.98611111, 7.98611111, 3.47222222, 3.125 , 7.63888889,
11.11111111, 18.05555556, 0.34722222, 3.47222222])CGMData_auto_wholeday_hyper_crit_average_percentages_over_all_days = \
CGMData_auto_wholeday_hyper_crit_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_hyper_crit_average_percentages_over_all_days.item()4.760194307608101Manual Mode / Whole Day / Normal¶
CGMData_manual_wholeday_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_manual_wholeday_normal_df.describe()Loading...
CGMData_manual_wholeday_normal_df[0:50]Loading...
CGMData_manual_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 60
1 149
2 143
3 193
4 222
5 232
6 182
7 225
8 228
9 132
10 154
11 123
12 183
13 168
14 145
15 66
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_normal_percentages_per_day_series = \
CGMData_manual_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_normal_percentages_per_day_series0 20.833333
1 51.736111
2 49.652778
3 67.013889
4 77.083333
5 80.555556
6 63.194444
7 78.125000
8 79.166667
9 45.833333
10 53.472222
11 42.708333
12 63.541667
13 58.333333
14 50.347222
15 22.916667
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_normal_percentages_per_day_array = \
CGMData_manual_wholeday_normal_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_normal_percentages_per_day_arrayarray([20.83333333, 51.73611111, 49.65277778, 67.01388889, 77.08333333,
80.55555556, 63.19444444, 78.125 , 79.16666667, 45.83333333,
53.47222222, 42.70833333, 63.54166667, 58.33333333, 50.34722222,
22.91666667])CGMData_manual_wholeday_normal_average_percentages_over_all_days = \
CGMData_manual_wholeday_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_wholeday_normal_average_percentages_over_all_days.item()4.4557334428024085Auto Mode / Whole Day / Normal¶
CGMData_auto_wholeday_normal_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'normal')
)]
CGMData_auto_wholeday_normal_df.describe()Loading...
CGMData_auto_wholeday_normal_df[0:5]Loading...
CGMData_auto_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 80
1 184
2 199
3 212
4 228
5 185
6 220
7 231
8 262
9 149
10 154
11 177
12 148
13 188
14 216
15 138
16 240
17 184
18 226
19 137
20 125
21 246
22 185
23 219
24 232
25 156
26 154
27 178
28 188
29 269
30 226
31 226
32 263
33 248
34 219
35 206
36 181
37 207
38 223
39 261
40 236
41 201
42 254
43 250
44 213
45 225
46 177
47 223
48 196
49 105
50 153
51 189
52 168
53 126
54 223
55 156
56 216
57 193
58 239
59 222
60 141
61 179
62 209
63 186
64 191
65 218
66 178
67 224
68 204
69 279
70 209
71 185
72 201
73 193
74 223
75 245
76 195
77 160
78 204
79 224
80 183
81 175
82 245
83 206
84 246
85 206
86 248
87 187
88 284
89 195
90 190
91 191
92 172
93 206
94 272
95 261
96 215
97 179
98 223
99 209
100 246
101 224
102 263
103 256
104 200
105 250
106 133
107 209
108 202
109 228
110 220
111 247
112 254
113 254
114 238
115 229
116 241
117 262
118 223
119 244
120 194
121 206
122 229
123 186
124 224
125 229
126 189
127 203
128 208
129 249
130 213
131 226
132 188
133 191
134 233
135 229
136 183
137 190
138 226
139 219
140 164
141 200
142 117
143 184
144 199
145 161
146 3
147 197
148 208
149 148
150 184
151 215
152 142
153 192
154 230
155 214
156 164
157 188
158 161
159 222
160 222
161 175
162 193
163 184
164 244
165 215
166 146
167 187
168 157
169 172
170 198
171 219
172 222
173 130
174 191
175 193
176 157
177 224
178 182
179 123
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_normal_percentages_per_day_series = \
CGMData_auto_wholeday_normal_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_normal_percentages_per_day_series0 27.777778
1 63.888889
2 69.097222
3 73.611111
4 79.166667
5 64.236111
6 76.388889
7 80.208333
8 90.972222
9 51.736111
10 53.472222
11 61.458333
12 51.388889
13 65.277778
14 75.000000
15 47.916667
16 83.333333
17 63.888889
18 78.472222
19 47.569444
20 43.402778
21 85.416667
22 64.236111
23 76.041667
24 80.555556
25 54.166667
26 53.472222
27 61.805556
28 65.277778
29 93.402778
30 78.472222
31 78.472222
32 91.319444
33 86.111111
34 76.041667
35 71.527778
36 62.847222
37 71.875000
38 77.430556
39 90.625000
40 81.944444
41 69.791667
42 88.194444
43 86.805556
44 73.958333
45 78.125000
46 61.458333
47 77.430556
48 68.055556
49 36.458333
50 53.125000
51 65.625000
52 58.333333
53 43.750000
54 77.430556
55 54.166667
56 75.000000
57 67.013889
58 82.986111
59 77.083333
60 48.958333
61 62.152778
62 72.569444
63 64.583333
64 66.319444
65 75.694444
66 61.805556
67 77.777778
68 70.833333
69 96.875000
70 72.569444
71 64.236111
72 69.791667
73 67.013889
74 77.430556
75 85.069444
76 67.708333
77 55.555556
78 70.833333
79 77.777778
80 63.541667
81 60.763889
82 85.069444
83 71.527778
84 85.416667
85 71.527778
86 86.111111
87 64.930556
88 98.611111
89 67.708333
90 65.972222
91 66.319444
92 59.722222
93 71.527778
94 94.444444
95 90.625000
96 74.652778
97 62.152778
98 77.430556
99 72.569444
100 85.416667
101 77.777778
102 91.319444
103 88.888889
104 69.444444
105 86.805556
106 46.180556
107 72.569444
108 70.138889
109 79.166667
110 76.388889
111 85.763889
112 88.194444
113 88.194444
114 82.638889
115 79.513889
116 83.680556
117 90.972222
118 77.430556
119 84.722222
120 67.361111
121 71.527778
122 79.513889
123 64.583333
124 77.777778
125 79.513889
126 65.625000
127 70.486111
128 72.222222
129 86.458333
130 73.958333
131 78.472222
132 65.277778
133 66.319444
134 80.902778
135 79.513889
136 63.541667
137 65.972222
138 78.472222
139 76.041667
140 56.944444
141 69.444444
142 40.625000
143 63.888889
144 69.097222
145 55.902778
146 1.041667
147 68.402778
148 72.222222
149 51.388889
150 63.888889
151 74.652778
152 49.305556
153 66.666667
154 79.861111
155 74.305556
156 56.944444
157 65.277778
158 55.902778
159 77.083333
160 77.083333
161 60.763889
162 67.013889
163 63.888889
164 84.722222
165 74.652778
166 50.694444
167 64.930556
168 54.513889
169 59.722222
170 68.750000
171 76.041667
172 77.083333
173 45.138889
174 66.319444
175 67.013889
176 54.513889
177 77.777778
178 63.194444
179 42.708333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_normal_percentages_per_day_array = \
CGMData_auto_wholeday_normal_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_normal_percentages_per_day_arrayarray([27.77777778, 63.88888889, 69.09722222, 73.61111111, 79.16666667,
64.23611111, 76.38888889, 80.20833333, 90.97222222, 51.73611111,
53.47222222, 61.45833333, 51.38888889, 65.27777778, 75. ,
47.91666667, 83.33333333, 63.88888889, 78.47222222, 47.56944444,
43.40277778, 85.41666667, 64.23611111, 76.04166667, 80.55555556,
54.16666667, 53.47222222, 61.80555556, 65.27777778, 93.40277778,
78.47222222, 78.47222222, 91.31944444, 86.11111111, 76.04166667,
71.52777778, 62.84722222, 71.875 , 77.43055556, 90.625 ,
81.94444444, 69.79166667, 88.19444444, 86.80555556, 73.95833333,
78.125 , 61.45833333, 77.43055556, 68.05555556, 36.45833333,
53.125 , 65.625 , 58.33333333, 43.75 , 77.43055556,
54.16666667, 75. , 67.01388889, 82.98611111, 77.08333333,
48.95833333, 62.15277778, 72.56944444, 64.58333333, 66.31944444,
75.69444444, 61.80555556, 77.77777778, 70.83333333, 96.875 ,
72.56944444, 64.23611111, 69.79166667, 67.01388889, 77.43055556,
85.06944444, 67.70833333, 55.55555556, 70.83333333, 77.77777778,
63.54166667, 60.76388889, 85.06944444, 71.52777778, 85.41666667,
71.52777778, 86.11111111, 64.93055556, 98.61111111, 67.70833333,
65.97222222, 66.31944444, 59.72222222, 71.52777778, 94.44444444,
90.625 , 74.65277778, 62.15277778, 77.43055556, 72.56944444,
85.41666667, 77.77777778, 91.31944444, 88.88888889, 69.44444444,
86.80555556, 46.18055556, 72.56944444, 70.13888889, 79.16666667,
76.38888889, 85.76388889, 88.19444444, 88.19444444, 82.63888889,
79.51388889, 83.68055556, 90.97222222, 77.43055556, 84.72222222,
67.36111111, 71.52777778, 79.51388889, 64.58333333, 77.77777778,
79.51388889, 65.625 , 70.48611111, 72.22222222, 86.45833333,
73.95833333, 78.47222222, 65.27777778, 66.31944444, 80.90277778,
79.51388889, 63.54166667, 65.97222222, 78.47222222, 76.04166667,
56.94444444, 69.44444444, 40.625 , 63.88888889, 69.09722222,
55.90277778, 1.04166667, 68.40277778, 72.22222222, 51.38888889,
63.88888889, 74.65277778, 49.30555556, 66.66666667, 79.86111111,
74.30555556, 56.94444444, 65.27777778, 55.90277778, 77.08333333,
77.08333333, 60.76388889, 67.01388889, 63.88888889, 84.72222222,
74.65277778, 50.69444444, 64.93055556, 54.51388889, 59.72222222,
68.75 , 76.04166667, 77.08333333, 45.13888889, 66.31944444,
67.01388889, 54.51388889, 77.77777778, 63.19444444, 42.70833333])CGMData_auto_wholeday_normal_average_percentages_over_all_days = \
CGMData_auto_wholeday_normal_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_normal_average_percentages_over_all_days.item()62.15961959496442Manual Mode / Whole Day / Secondary¶
CGMData_manual_wholeday_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_manual_wholeday_secondary_df.describe()Loading...
CGMData_manual_wholeday_secondary_df[0:50]Loading...
CGMData_manual_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 41
1 128
2 70
3 179
4 166
5 191
6 161
7 136
8 184
9 100
10 126
11 90
12 162
13 127
14 94
15 50
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_secondary_percentages_per_day_series = \
CGMData_manual_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_secondary_percentages_per_day_series0 14.236111
1 44.444444
2 24.305556
3 62.152778
4 57.638889
5 66.319444
6 55.902778
7 47.222222
8 63.888889
9 34.722222
10 43.750000
11 31.250000
12 56.250000
13 44.097222
14 32.638889
15 17.361111
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_secondary_percentages_per_day_array = \
CGMData_manual_wholeday_secondary_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_secondary_percentages_per_day_arrayarray([14.23611111, 44.44444444, 24.30555556, 62.15277778, 57.63888889,
66.31944444, 55.90277778, 47.22222222, 63.88888889, 34.72222222,
43.75 , 31.25 , 56.25 , 44.09722222, 32.63888889,
17.36111111])CGMData_manual_wholeday_secondary_average_percentages_over_all_days = \
CGMData_manual_wholeday_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_wholeday_secondary_average_percentages_over_all_days.item()3.429460864805692Auto Mode / Whole Day / Secondary¶
CGMData_auto_wholeday_secondary_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification (Secondary)'] == 'secondary')
)]
CGMData_auto_wholeday_secondary_df.describe()Loading...
CGMData_auto_wholeday_secondary_df[0:50]Loading...
CGMData_auto_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 60
1 136
2 140
3 187
4 180
5 136
6 182
7 217
8 216
9 65
10 99
11 141
12 57
13 152
14 167
15 100
16 218
17 178
18 198
19 91
20 80
21 165
22 155
23 167
24 222
25 129
26 143
27 146
28 144
29 218
30 193
31 184
32 180
33 191
34 178
35 164
36 112
37 149
38 208
39 205
40 187
41 122
42 192
43 213
44 129
45 194
46 149
47 179
48 166
49 60
50 122
51 114
52 124
53 85
54 158
55 131
56 183
57 123
58 211
59 208
60 110
61 153
62 155
63 120
64 136
65 179
66 140
67 180
68 181
69 269
70 167
71 115
72 161
73 143
74 217
75 160
76 149
77 121
78 157
79 180
80 163
81 146
82 172
83 162
84 233
85 166
86 220
87 175
88 244
89 164
90 140
91 162
92 147
93 155
94 253
95 206
96 165
97 172
98 189
99 132
100 210
101 191
102 225
103 195
104 150
105 226
106 94
107 131
108 178
109 165
110 149
111 184
112 222
113 226
114 173
115 171
116 176
117 204
118 198
119 211
120 129
121 190
122 203
123 162
124 168
125 192
126 118
127 159
128 164
129 184
130 186
131 166
132 170
133 175
134 132
135 166
136 145
137 130
138 163
139 191
140 109
141 166
142 90
143 142
144 153
145 120
146 138
147 144
148 98
149 174
150 178
151 116
152 149
153 156
154 169
155 103
156 127
157 149
158 185
159 136
160 141
161 161
162 159
163 208
164 151
165 93
166 143
167 121
168 106
169 141
170 178
171 166
172 73
173 153
174 156
175 108
176 167
177 131
178 90
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_secondary_percentages_per_day_series = \
CGMData_auto_wholeday_secondary_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_secondary_percentages_per_day_series0 20.833333
1 47.222222
2 48.611111
3 64.930556
4 62.500000
5 47.222222
6 63.194444
7 75.347222
8 75.000000
9 22.569444
10 34.375000
11 48.958333
12 19.791667
13 52.777778
14 57.986111
15 34.722222
16 75.694444
17 61.805556
18 68.750000
19 31.597222
20 27.777778
21 57.291667
22 53.819444
23 57.986111
24 77.083333
25 44.791667
26 49.652778
27 50.694444
28 50.000000
29 75.694444
30 67.013889
31 63.888889
32 62.500000
33 66.319444
34 61.805556
35 56.944444
36 38.888889
37 51.736111
38 72.222222
39 71.180556
40 64.930556
41 42.361111
42 66.666667
43 73.958333
44 44.791667
45 67.361111
46 51.736111
47 62.152778
48 57.638889
49 20.833333
50 42.361111
51 39.583333
52 43.055556
53 29.513889
54 54.861111
55 45.486111
56 63.541667
57 42.708333
58 73.263889
59 72.222222
60 38.194444
61 53.125000
62 53.819444
63 41.666667
64 47.222222
65 62.152778
66 48.611111
67 62.500000
68 62.847222
69 93.402778
70 57.986111
71 39.930556
72 55.902778
73 49.652778
74 75.347222
75 55.555556
76 51.736111
77 42.013889
78 54.513889
79 62.500000
80 56.597222
81 50.694444
82 59.722222
83 56.250000
84 80.902778
85 57.638889
86 76.388889
87 60.763889
88 84.722222
89 56.944444
90 48.611111
91 56.250000
92 51.041667
93 53.819444
94 87.847222
95 71.527778
96 57.291667
97 59.722222
98 65.625000
99 45.833333
100 72.916667
101 66.319444
102 78.125000
103 67.708333
104 52.083333
105 78.472222
106 32.638889
107 45.486111
108 61.805556
109 57.291667
110 51.736111
111 63.888889
112 77.083333
113 78.472222
114 60.069444
115 59.375000
116 61.111111
117 70.833333
118 68.750000
119 73.263889
120 44.791667
121 65.972222
122 70.486111
123 56.250000
124 58.333333
125 66.666667
126 40.972222
127 55.208333
128 56.944444
129 63.888889
130 64.583333
131 57.638889
132 59.027778
133 60.763889
134 45.833333
135 57.638889
136 50.347222
137 45.138889
138 56.597222
139 66.319444
140 37.847222
141 57.638889
142 31.250000
143 49.305556
144 53.125000
145 41.666667
146 47.916667
147 50.000000
148 34.027778
149 60.416667
150 61.805556
151 40.277778
152 51.736111
153 54.166667
154 58.680556
155 35.763889
156 44.097222
157 51.736111
158 64.236111
159 47.222222
160 48.958333
161 55.902778
162 55.208333
163 72.222222
164 52.430556
165 32.291667
166 49.652778
167 42.013889
168 36.805556
169 48.958333
170 61.805556
171 57.638889
172 25.347222
173 53.125000
174 54.166667
175 37.500000
176 57.986111
177 45.486111
178 31.250000
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_secondary_percentages_per_day_array = \
CGMData_auto_wholeday_secondary_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_secondary_percentages_per_day_arrayarray([20.83333333, 47.22222222, 48.61111111, 64.93055556, 62.5 ,
47.22222222, 63.19444444, 75.34722222, 75. , 22.56944444,
34.375 , 48.95833333, 19.79166667, 52.77777778, 57.98611111,
34.72222222, 75.69444444, 61.80555556, 68.75 , 31.59722222,
27.77777778, 57.29166667, 53.81944444, 57.98611111, 77.08333333,
44.79166667, 49.65277778, 50.69444444, 50. , 75.69444444,
67.01388889, 63.88888889, 62.5 , 66.31944444, 61.80555556,
56.94444444, 38.88888889, 51.73611111, 72.22222222, 71.18055556,
64.93055556, 42.36111111, 66.66666667, 73.95833333, 44.79166667,
67.36111111, 51.73611111, 62.15277778, 57.63888889, 20.83333333,
42.36111111, 39.58333333, 43.05555556, 29.51388889, 54.86111111,
45.48611111, 63.54166667, 42.70833333, 73.26388889, 72.22222222,
38.19444444, 53.125 , 53.81944444, 41.66666667, 47.22222222,
62.15277778, 48.61111111, 62.5 , 62.84722222, 93.40277778,
57.98611111, 39.93055556, 55.90277778, 49.65277778, 75.34722222,
55.55555556, 51.73611111, 42.01388889, 54.51388889, 62.5 ,
56.59722222, 50.69444444, 59.72222222, 56.25 , 80.90277778,
57.63888889, 76.38888889, 60.76388889, 84.72222222, 56.94444444,
48.61111111, 56.25 , 51.04166667, 53.81944444, 87.84722222,
71.52777778, 57.29166667, 59.72222222, 65.625 , 45.83333333,
72.91666667, 66.31944444, 78.125 , 67.70833333, 52.08333333,
78.47222222, 32.63888889, 45.48611111, 61.80555556, 57.29166667,
51.73611111, 63.88888889, 77.08333333, 78.47222222, 60.06944444,
59.375 , 61.11111111, 70.83333333, 68.75 , 73.26388889,
44.79166667, 65.97222222, 70.48611111, 56.25 , 58.33333333,
66.66666667, 40.97222222, 55.20833333, 56.94444444, 63.88888889,
64.58333333, 57.63888889, 59.02777778, 60.76388889, 45.83333333,
57.63888889, 50.34722222, 45.13888889, 56.59722222, 66.31944444,
37.84722222, 57.63888889, 31.25 , 49.30555556, 53.125 ,
41.66666667, 47.91666667, 50. , 34.02777778, 60.41666667,
61.80555556, 40.27777778, 51.73611111, 54.16666667, 58.68055556,
35.76388889, 44.09722222, 51.73611111, 64.23611111, 47.22222222,
48.95833333, 55.90277778, 55.20833333, 72.22222222, 52.43055556,
32.29166667, 49.65277778, 42.01388889, 36.80555556, 48.95833333,
61.80555556, 57.63888889, 25.34722222, 53.125 , 54.16666667,
37.5 , 57.98611111, 45.48611111, 31.25 ])CGMData_auto_wholeday_secondary_average_percentages_over_all_days = \
CGMData_auto_wholeday_secondary_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_secondary_average_percentages_over_all_days.item()48.71715927750411Manual Mode / Whole Day / Hypoglycemia Level 1¶
CGMData_manual_wholeday_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_manual_wholeday_hypo_1_df.describe()Loading...
CGMData_manual_wholeday_hypo_1_df[0:50]Loading...
CGMData_manual_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 32
1 13
2 6
3 24
4 9
5 16
6 11
7 9
8 9
9 16
10 16
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_hypo_1_percentages_per_day_series = \
CGMData_manual_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_hypo_1_percentages_per_day_series0 11.111111
1 4.513889
2 2.083333
3 8.333333
4 3.125000
5 5.555556
6 3.819444
7 3.125000
8 3.125000
9 5.555556
10 5.555556
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_hypo_1_percentages_per_day_array = \
CGMData_manual_wholeday_hypo_1_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_hypo_1_percentages_per_day_arrayarray([11.11111111, 4.51388889, 2.08333333, 8.33333333, 3.125 ,
5.55555556, 3.81944444, 3.125 , 3.125 , 5.55555556,
5.55555556])CGMData_manual_wholeday_hypo_1_average_percentages_over_all_days = \
CGMData_manual_wholeday_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_wholeday_hypo_1_average_percentages_over_all_days.item()0.2753831417624521Auto Mode / Whole Day / Hypoglycemia Level 1¶
CGMData_auto_wholeday_hypo_1_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 1')
)]
CGMData_auto_wholeday_hypo_1_df.describe()Loading...
CGMData_auto_wholeday_hypo_1_df[0:5]Loading...
CGMData_auto_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 17
1 10
2 4
3 2
4 9
5 7
6 14
7 3
8 10
9 12
10 10
11 7
12 2
13 38
14 37
15 3
16 17
17 8
18 7
19 10
20 3
21 3
22 5
23 7
24 14
25 12
26 23
27 2
28 5
29 3
30 8
31 7
32 2
33 3
34 5
35 11
36 1
37 12
38 54
39 12
40 4
41 7
42 10
43 21
44 5
45 10
46 2
47 21
48 44
49 17
50 34
51 12
52 9
53 13
54 6
55 8
56 39
57 8
58 11
59 9
60 13
61 1
62 12
63 10
64 7
65 7
66 5
67 2
68 25
69 3
70 2
71 4
72 5
73 5
74 14
75 5
76 8
77 3
78 8
79 19
80 2
81 14
82 7
83 14
84 34
85 6
86 10
87 14
88 13
89 6
90 11
91 5
92 5
93 7
94 2
95 18
96 16
97 17
98 6
99 17
100 2
101 3
102 17
103 4
104 20
105 9
106 18
107 29
108 9
109 5
110 23
111 43
112 13
113 11
114 5
115 13
116 2
117 15
118 6
119 11
120 3
121 6
122 16
123 8
124 5
125 6
126 12
127 6
128 11
129 30
130 27
131 6
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_hypo_1_percentages_per_day_series = \
CGMData_auto_wholeday_hypo_1_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_hypo_1_percentages_per_day_series0 5.902778
1 3.472222
2 1.388889
3 0.694444
4 3.125000
5 2.430556
6 4.861111
7 1.041667
8 3.472222
9 4.166667
10 3.472222
11 2.430556
12 0.694444
13 13.194444
14 12.847222
15 1.041667
16 5.902778
17 2.777778
18 2.430556
19 3.472222
20 1.041667
21 1.041667
22 1.736111
23 2.430556
24 4.861111
25 4.166667
26 7.986111
27 0.694444
28 1.736111
29 1.041667
30 2.777778
31 2.430556
32 0.694444
33 1.041667
34 1.736111
35 3.819444
36 0.347222
37 4.166667
38 18.750000
39 4.166667
40 1.388889
41 2.430556
42 3.472222
43 7.291667
44 1.736111
45 3.472222
46 0.694444
47 7.291667
48 15.277778
49 5.902778
50 11.805556
51 4.166667
52 3.125000
53 4.513889
54 2.083333
55 2.777778
56 13.541667
57 2.777778
58 3.819444
59 3.125000
60 4.513889
61 0.347222
62 4.166667
63 3.472222
64 2.430556
65 2.430556
66 1.736111
67 0.694444
68 8.680556
69 1.041667
70 0.694444
71 1.388889
72 1.736111
73 1.736111
74 4.861111
75 1.736111
76 2.777778
77 1.041667
78 2.777778
79 6.597222
80 0.694444
81 4.861111
82 2.430556
83 4.861111
84 11.805556
85 2.083333
86 3.472222
87 4.861111
88 4.513889
89 2.083333
90 3.819444
91 1.736111
92 1.736111
93 2.430556
94 0.694444
95 6.250000
96 5.555556
97 5.902778
98 2.083333
99 5.902778
100 0.694444
101 1.041667
102 5.902778
103 1.388889
104 6.944444
105 3.125000
106 6.250000
107 10.069444
108 3.125000
109 1.736111
110 7.986111
111 14.930556
112 4.513889
113 3.819444
114 1.736111
115 4.513889
116 0.694444
117 5.208333
118 2.083333
119 3.819444
120 1.041667
121 2.083333
122 5.555556
123 2.777778
124 1.736111
125 2.083333
126 4.166667
127 2.083333
128 3.819444
129 10.416667
130 9.375000
131 2.083333
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_hypo_1_percentages_per_day_array = \
CGMData_auto_wholeday_hypo_1_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_hypo_1_percentages_per_day_arrayarray([ 5.90277778, 3.47222222, 1.38888889, 0.69444444, 3.125 ,
2.43055556, 4.86111111, 1.04166667, 3.47222222, 4.16666667,
3.47222222, 2.43055556, 0.69444444, 13.19444444, 12.84722222,
1.04166667, 5.90277778, 2.77777778, 2.43055556, 3.47222222,
1.04166667, 1.04166667, 1.73611111, 2.43055556, 4.86111111,
4.16666667, 7.98611111, 0.69444444, 1.73611111, 1.04166667,
2.77777778, 2.43055556, 0.69444444, 1.04166667, 1.73611111,
3.81944444, 0.34722222, 4.16666667, 18.75 , 4.16666667,
1.38888889, 2.43055556, 3.47222222, 7.29166667, 1.73611111,
3.47222222, 0.69444444, 7.29166667, 15.27777778, 5.90277778,
11.80555556, 4.16666667, 3.125 , 4.51388889, 2.08333333,
2.77777778, 13.54166667, 2.77777778, 3.81944444, 3.125 ,
4.51388889, 0.34722222, 4.16666667, 3.47222222, 2.43055556,
2.43055556, 1.73611111, 0.69444444, 8.68055556, 1.04166667,
0.69444444, 1.38888889, 1.73611111, 1.73611111, 4.86111111,
1.73611111, 2.77777778, 1.04166667, 2.77777778, 6.59722222,
0.69444444, 4.86111111, 2.43055556, 4.86111111, 11.80555556,
2.08333333, 3.47222222, 4.86111111, 4.51388889, 2.08333333,
3.81944444, 1.73611111, 1.73611111, 2.43055556, 0.69444444,
6.25 , 5.55555556, 5.90277778, 2.08333333, 5.90277778,
0.69444444, 1.04166667, 5.90277778, 1.38888889, 6.94444444,
3.125 , 6.25 , 10.06944444, 3.125 , 1.73611111,
7.98611111, 14.93055556, 4.51388889, 3.81944444, 1.73611111,
4.51388889, 0.69444444, 5.20833333, 2.08333333, 3.81944444,
1.04166667, 2.08333333, 5.55555556, 2.77777778, 1.73611111,
2.08333333, 4.16666667, 2.08333333, 3.81944444, 10.41666667,
9.375 , 2.08333333])CGMData_auto_wholeday_hypo_1_average_percentages_over_all_days = \
CGMData_auto_wholeday_hypo_1_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_hypo_1_average_percentages_over_all_days.item()2.540024630541872Manual Mode / Whole Day / Hypoglycemia Level 2¶
CGMData_manual_wholeday_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Manual Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_manual_wholeday_hypo_2_df.describe()Loading...
CGMData_manual_wholeday_hypo_2_df[0:50]Loading...
CGMData_manual_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_manual_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 13
1 19
2 9
3 14
4 8
5 2
6 7
7 12
8 4
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_manual_wholeday_hypo_2_percentages_per_day_series = \
CGMData_manual_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_manual_wholeday_hypo_2_percentages_per_day_series0 4.513889
1 6.597222
2 3.125000
3 4.861111
4 2.777778
5 0.694444
6 2.430556
7 4.166667
8 1.388889
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_manual_wholeday_hypo_2_percentages_per_day_array = \
CGMData_manual_wholeday_hypo_2_percentages_per_day_series.to_numpy()
CGMData_manual_wholeday_hypo_2_percentages_per_day_arrayarray([4.51388889, 6.59722222, 3.125 , 4.86111111, 2.77777778,
0.69444444, 2.43055556, 4.16666667, 1.38888889])CGMData_manual_wholeday_hypo_2_average_percentages_over_all_days = \
CGMData_manual_wholeday_hypo_2_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_manual_wholeday_hypo_2_average_percentages_over_all_days.item()0.150519978106185Auto Mode / Whole Day / Hypoglycemia Level 2¶
CGMData_auto_wholeday_hypo_2_df = \
CGMData_df[(
(CGMData_df['Sensor Mode'] == 'Auto Mode') & \
#(CGMData_df['Time Interval'] == 'daytime') & \
(CGMData_df['Sensor Glucose Classification'] == 'hypoglycemia level 2')
)]
CGMData_auto_wholeday_hypo_2_df.describe()Loading...
CGMData_auto_wholeday_hypo_2_df[0:5]Loading...
CGMData_auto_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()Loading...
CGMData_auto_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]']0 2
1 3
2 8
3 12
4 10
5 4
6 12
7 7
8 5
9 6
10 3
11 4
12 1
13 16
14 7
15 1
16 2
17 7
18 11
19 3
20 7
21 8
22 1
23 1
24 8
25 6
26 3
27 11
28 5
29 9
30 7
31 4
32 12
33 3
34 4
35 1
36 45
37 7
38 6
39 1
40 10
41 20
42 28
43 9
44 10
45 2
46 10
47 12
48 16
49 2
50 1
51 2
52 14
53 5
54 8
55 12
56 7
57 12
58 11
59 27
60 4
61 5
62 2
63 14
64 8
65 1
66 1
67 8
68 4
69 9
70 3
71 9
72 21
73 12
74 12
75 5
76 6
77 3
78 5
79 9
80 24
81 13
82 14
83 2
84 6
85 13
86 1
87 5
88 9
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: int64CGMData_auto_wholeday_hypo_2_percentages_per_day_series = \
CGMData_auto_wholeday_hypo_2_df.groupby(
'Day Number',
as_index=False)['Sensor Glucose (mg/dL) [Interpolated]'].count()['Sensor Glucose (mg/dL) [Interpolated]'] / 288 * 100
CGMData_auto_wholeday_hypo_2_percentages_per_day_series0 0.694444
1 1.041667
2 2.777778
3 4.166667
4 3.472222
5 1.388889
6 4.166667
7 2.430556
8 1.736111
9 2.083333
10 1.041667
11 1.388889
12 0.347222
13 5.555556
14 2.430556
15 0.347222
16 0.694444
17 2.430556
18 3.819444
19 1.041667
20 2.430556
21 2.777778
22 0.347222
23 0.347222
24 2.777778
25 2.083333
26 1.041667
27 3.819444
28 1.736111
29 3.125000
30 2.430556
31 1.388889
32 4.166667
33 1.041667
34 1.388889
35 0.347222
36 15.625000
37 2.430556
38 2.083333
39 0.347222
40 3.472222
41 6.944444
42 9.722222
43 3.125000
44 3.472222
45 0.694444
46 3.472222
47 4.166667
48 5.555556
49 0.694444
50 0.347222
51 0.694444
52 4.861111
53 1.736111
54 2.777778
55 4.166667
56 2.430556
57 4.166667
58 3.819444
59 9.375000
60 1.388889
61 1.736111
62 0.694444
63 4.861111
64 2.777778
65 0.347222
66 0.347222
67 2.777778
68 1.388889
69 3.125000
70 1.041667
71 3.125000
72 7.291667
73 4.166667
74 4.166667
75 1.736111
76 2.083333
77 1.041667
78 1.736111
79 3.125000
80 8.333333
81 4.513889
82 4.861111
83 0.694444
84 2.083333
85 4.513889
86 0.347222
87 1.736111
88 3.125000
Name: Sensor Glucose (mg/dL) [Interpolated], dtype: float64CGMData_auto_wholeday_hypo_2_percentages_per_day_array = \
CGMData_auto_wholeday_hypo_2_percentages_per_day_series.to_numpy()
CGMData_auto_wholeday_hypo_2_percentages_per_day_arrayarray([ 0.69444444, 1.04166667, 2.77777778, 4.16666667, 3.47222222,
1.38888889, 4.16666667, 2.43055556, 1.73611111, 2.08333333,
1.04166667, 1.38888889, 0.34722222, 5.55555556, 2.43055556,
0.34722222, 0.69444444, 2.43055556, 3.81944444, 1.04166667,
2.43055556, 2.77777778, 0.34722222, 0.34722222, 2.77777778,
2.08333333, 1.04166667, 3.81944444, 1.73611111, 3.125 ,
2.43055556, 1.38888889, 4.16666667, 1.04166667, 1.38888889,
0.34722222, 15.625 , 2.43055556, 2.08333333, 0.34722222,
3.47222222, 6.94444444, 9.72222222, 3.125 , 3.47222222,
0.69444444, 3.47222222, 4.16666667, 5.55555556, 0.69444444,
0.34722222, 0.69444444, 4.86111111, 1.73611111, 2.77777778,
4.16666667, 2.43055556, 4.16666667, 3.81944444, 9.375 ,
1.38888889, 1.73611111, 0.69444444, 4.86111111, 2.77777778,
0.34722222, 0.34722222, 2.77777778, 1.38888889, 3.125 ,
1.04166667, 3.125 , 7.29166667, 4.16666667, 4.16666667,
1.73611111, 2.08333333, 1.04166667, 1.73611111, 3.125 ,
8.33333333, 4.51388889, 4.86111111, 0.69444444, 2.08333333,
4.51388889, 0.34722222, 1.73611111, 3.125 ])CGMData_auto_wholeday_hypo_2_average_percentages_over_all_days = \
CGMData_auto_wholeday_hypo_2_percentages_per_day_array.sum() / number_of_days_in_data
CGMData_auto_wholeday_hypo_2_average_percentages_over_all_days.item()1.2298166392993979Create Output Matrix¶
Compile Results into Lists¶
manual_mode_data_list = \
[
CGMData_manual_overnight_hyper_average_percentages_over_all_days,
CGMData_manual_overnight_hyper_crit_average_percentages_over_all_days,
CGMData_manual_overnight_normal_average_percentages_over_all_days,
CGMData_manual_overnight_secondary_average_percentages_over_all_days,
CGMData_manual_overnight_hypo_1_average_percentages_over_all_days,
CGMData_manual_overnight_hypo_2_average_percentages_over_all_days,
CGMData_manual_daytime_hyper_average_percentages_over_all_days,
CGMData_manual_daytime_hyper_crit_average_percentages_over_all_days,
CGMData_manual_daytime_normal_average_percentages_over_all_days,
CGMData_manual_daytime_secondary_average_percentages_over_all_days,
CGMData_manual_daytime_hypo_1_average_percentages_over_all_days,
CGMData_manual_daytime_hypo_2_average_percentages_over_all_days,
CGMData_manual_wholeday_hyper_average_percentages_over_all_days,
CGMData_manual_wholeday_hyper_crit_average_percentages_over_all_days,
CGMData_manual_wholeday_normal_average_percentages_over_all_days,
CGMData_manual_wholeday_secondary_average_percentages_over_all_days,
CGMData_manual_wholeday_hypo_1_average_percentages_over_all_days,
CGMData_manual_wholeday_hypo_2_average_percentages_over_all_days
]
manual_mode_data_list[np.float64(0.28393541324575805),
np.float64(0.0769704433497537),
np.float64(1.4367816091954027),
np.float64(1.0690339354132457),
np.float64(0.04960317460317461),
np.float64(0.0),
np.float64(1.3119184455391353),
np.float64(0.725232621784346),
np.float64(3.0189518336070056),
np.float64(2.3604269293924465),
np.float64(0.2257799671592775),
np.float64(0.150519978106185),
np.float64(1.5958538587848932),
np.float64(0.8022030651340996),
np.float64(4.4557334428024085),
np.float64(3.429460864805692),
np.float64(0.2753831417624521),
np.float64(0.150519978106185)]auto_mode_data_list = \
[
CGMData_auto_overnight_hyper_average_percentages_over_all_days,
CGMData_auto_overnight_hyper_crit_average_percentages_over_all_days,
CGMData_auto_overnight_normal_average_percentages_over_all_days,
CGMData_auto_overnight_secondary_average_percentages_over_all_days,
CGMData_auto_overnight_hypo_1_average_percentages_over_all_days,
CGMData_auto_overnight_hypo_2_average_percentages_over_all_days,
CGMData_auto_daytime_hyper_average_percentages_over_all_days,
CGMData_auto_daytime_hyper_crit_average_percentages_over_all_days,
CGMData_auto_daytime_normal_average_percentages_over_all_days,
CGMData_auto_daytime_secondary_average_percentages_over_all_days,
CGMData_auto_daytime_hypo_1_average_percentages_over_all_days,
CGMData_auto_daytime_hypo_2_average_percentages_over_all_days,
CGMData_auto_wholeday_hyper_average_percentages_over_all_days,
CGMData_auto_wholeday_hyper_crit_average_percentages_over_all_days,
CGMData_auto_wholeday_normal_average_percentages_over_all_days,
CGMData_auto_wholeday_secondary_average_percentages_over_all_days,
CGMData_auto_wholeday_hypo_1_average_percentages_over_all_days,
CGMData_auto_wholeday_hypo_2_average_percentages_over_all_days
]
auto_mode_data_list[np.float64(2.1756978653530377),
np.float64(0.4053776683087028),
np.float64(18.756841817186643),
np.float64(16.379310344827587),
np.float64(0.4019567597153804),
np.float64(0.19157088122605362),
np.float64(14.516625615763544),
np.float64(4.354816639299398),
np.float64(43.402777777777786),
np.float64(32.337848932676515),
np.float64(2.1380678708264913),
np.float64(1.0382457580733442),
np.float64(16.692323481116585),
np.float64(4.760194307608101),
np.float64(62.15961959496442),
np.float64(48.71715927750411),
np.float64(2.540024630541872),
np.float64(1.2298166392993979)]Zip the Lists Together¶
zipped_lists = list(zip(manual_mode_data_list, auto_mode_data_list))transposed_lists = list(zip(*zipped_lists))Create Output Dataframe¶
column_names = \
[
'Overnight Hyperglycemia [% of time]',
'Overnight Hyperglycemia Critical [% of time]',
'Overnight Normal [% of time]',
'Overnight Secondary [% of time]',
'Overnight Hypoglycemia Level 1 [% of time]',
'Overnight Hypoglycemia Level 2 [% of time]',
'Daytime Hyperglycemia [% of time]',
'Daytime Hyperglycemia Critical [% of time]',
'Daytime Normal [% of time]',
'Daytime Secondary [% of time]',
'Daytime Hypoglycemia Level 1 [% of time]',
'Daytime Hypoglycemia Level 2 [% of time]',
'Whole Day Hyperglycemia [% of time]',
'Whole Day Hyperglycemia Critical [% of time]',
'Whole Day Normal [% of time]',
'Whole Day Secondary [% of time]',
'Whole Day Hypoglycemia Level 1 [% of time]',
'Whole Day Hypoglycemia Level 2 [% of time]'
]column_names_overnight = \
[
'Overnight Hyperglycemia [% of time]',
'Overnight Hyperglycemia Critical [% of time]',
'Overnight Normal [% of time]',
'Overnight Secondary [% of time]',
'Overnight Hypoglycemia Level 1 [% of time]',
'Overnight Hypoglycemia Level 2 [% of time]'
]column_names_daytime = \
[
'Daytime Hyperglycemia [% of time]',
'Daytime Hyperglycemia Critical [% of time]',
'Daytime Normal [% of time]',
'Daytime Secondary [% of time]',
'Daytime Hypoglycemia Level 1 [% of time]',
'Daytime Hypoglycemia Level 2 [% of time]'
]column_names_whole_day = \
[
'Whole Day Hyperglycemia [% of time]',
'Whole Day Hyperglycemia Critical [% of time]',
'Whole Day Normal [% of time]',
'Whole Day Secondary [% of time]',
'Whole Day Hypoglycemia Level 1 [% of time]',
'Whole Day Hypoglycemia Level 2 [% of time]'
]row_names = ['Manual Mode', 'Auto Mode']output_df = pd.DataFrame(
transposed_lists,
columns=column_names,
index=row_names
)output_dfLoading...
output_df[column_names_overnight]Loading...
output_df[column_names_daytime]Loading...
output_df[column_names_whole_day]Loading...
output_df.columnsIndex(['Overnight Hyperglycemia [% of time]',
'Overnight Hyperglycemia Critical [% of time]',
'Overnight Normal [% of time]', 'Overnight Secondary [% of time]',
'Overnight Hypoglycemia Level 1 [% of time]',
'Overnight Hypoglycemia Level 2 [% of time]',
'Daytime Hyperglycemia [% of time]',
'Daytime Hyperglycemia Critical [% of time]',
'Daytime Normal [% of time]', 'Daytime Secondary [% of time]',
'Daytime Hypoglycemia Level 1 [% of time]',
'Daytime Hypoglycemia Level 2 [% of time]',
'Whole Day Hyperglycemia [% of time]',
'Whole Day Hyperglycemia Critical [% of time]',
'Whole Day Normal [% of time]', 'Whole Day Secondary [% of time]',
'Whole Day Hypoglycemia Level 1 [% of time]',
'Whole Day Hypoglycemia Level 2 [% of time]'],
dtype='object')output_df.columns.shape(18,)Save Output into File¶
output_df.to_csv('Result.csv', header=False, index=False)