Overview

Dataset statistics

Number of variables20
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.4 KiB
Average record size in memory160.0 B

Variable types

NUM16
BOOL3
CAT1

Reproduction

Analysis started2020-08-25 01:13:27.506035
Analysis finished2020-08-25 01:14:09.931188
Duration42.43 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

total intl minutes is highly correlated with total intl chargeHigh correlation
total intl charge is highly correlated with total intl minutesHigh correlation
total night minutes is highly correlated with total night chargeHigh correlation
total night charge is highly correlated with total night minutesHigh correlation
total day minutes is highly correlated with total day chargeHigh correlation
total day charge is highly correlated with total day minutesHigh correlation
total eve minutes is highly correlated with total eve chargeHigh correlation
total eve charge is highly correlated with total eve minutesHigh correlation
phone number has unique values Unique
state has 72 (1.4%) zeros Zeros
number customer service calls has 1023 (20.5%) zeros Zeros

Variables

state
Real number (ℝ≥0)

ZEROS

Distinct count51
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.9984
Minimum0
Maximum50
Zeros72
Zeros (%)1.4%
Memory size39.2 KiB
2020-08-25T01:14:09.977304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median26
Q339
95-th percentile49
Maximum50
Range50
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.8034802
Coefficient of variation (CV)0.5693996631
Kurtosis-1.185987798
Mean25.9984
Median Absolute Deviation (MAD)13
Skewness-0.05797513823
Sum129992
Variance219.143026
2020-08-25T01:14:10.080704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
491583.2%
 
231252.5%
 
11242.5%
 
131192.4%
 
451182.4%
 
351162.3%
 
431162.3%
 
501152.3%
 
341142.3%
 
371142.3%
 
441122.2%
 
311122.2%
 
481062.1%
 
191032.1%
 
211032.1%
 
221032.1%
 
201022.0%
 
461012.0%
 
16992.0%
 
17992.0%
 
26992.0%
 
25992.0%
 
39992.0%
 
6992.0%
 
47982.0%
 
Other values (26)224744.9%
 
ValueCountFrequency (%) 
0721.4%
 
11242.5%
 
2921.8%
 
3891.8%
 
4521.0%
 
5961.9%
 
6992.0%
 
7881.8%
 
8941.9%
 
9901.8%
 
ValueCountFrequency (%) 
501152.3%
 
491583.2%
 
481062.1%
 
47982.0%
 
461012.0%
 
451182.4%
 
441122.2%
 
431162.3%
 
42891.8%
 
41851.7%
 

total intl calls
Real number (ℝ≥0)

Distinct count21
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4352
Minimum0.0
Maximum20.0
Zeros24
Zeros (%)0.5%
Memory size39.2 KiB
2020-08-25T01:14:10.200366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.456788172
Coefficient of variation (CV)0.5539295121
Kurtosis3.268183647
Mean4.4352
Median Absolute Deviation (MAD)1
Skewness1.360692479
Sum22176
Variance6.035808122
2020-08-25T01:14:10.315004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
399219.8%
 
495319.1%
 
274314.9%
 
570614.1%
 
64959.9%
 
73086.2%
 
12655.3%
 
81723.4%
 
91483.0%
 
10761.5%
 
11450.9%
 
0240.5%
 
12230.5%
 
13190.4%
 
1590.2%
 
1670.1%
 
1460.1%
 
1840.1%
 
192< 0.1%
 
172< 0.1%
 
201< 0.1%
 
ValueCountFrequency (%) 
0240.5%
 
12655.3%
 
274314.9%
 
399219.8%
 
495319.1%
 
570614.1%
 
64959.9%
 
73086.2%
 
81723.4%
 
91483.0%
 
ValueCountFrequency (%) 
201< 0.1%
 
192< 0.1%
 
1840.1%
 
172< 0.1%
 
1670.1%
 
1590.2%
 
1460.1%
 
13190.4%
 
12230.5%
 
11450.9%
 

total night charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1028
Unique (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.017732
Minimum0.0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:10.438723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.28
Q17.51
median9.02
Q310.56
95-th percentile12.7505
Maximum17.77
Range17.77
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation2.273762656
Coefficient of variation (CV)0.2521435163
Kurtosis0.08237761539
Mean9.017732
Median Absolute Deviation (MAD)1.52
Skewness0.01928674434
Sum45088.66
Variance5.169996615
2020-08-25T01:14:10.558323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.66190.4%
 
8.47190.4%
 
10.26180.4%
 
10.8180.4%
 
8.15180.4%
 
9.4180.4%
 
9.63180.4%
 
10.49170.3%
 
9.45170.3%
 
8.88160.3%
 
9.76160.3%
 
10.35160.3%
 
9.65160.3%
 
8.28150.3%
 
9.23150.3%
 
7.69150.3%
 
9.18150.3%
 
9.09150.3%
 
8.82150.3%
 
7.15140.3%
 
9.27140.3%
 
9.14140.3%
 
8.57140.3%
 
7.92140.3%
 
8.74140.3%
 
Other values (1003)460092.0%
 
ValueCountFrequency (%) 
01< 0.1%
 
1.041< 0.1%
 
1.971< 0.1%
 
2.031< 0.1%
 
2.11< 0.1%
 
2.131< 0.1%
 
2.252< 0.1%
 
2.291< 0.1%
 
2.41< 0.1%
 
2.431< 0.1%
 
ValueCountFrequency (%) 
17.771< 0.1%
 
17.191< 0.1%
 
17.171< 0.1%
 
16.991< 0.1%
 
16.551< 0.1%
 
16.421< 0.1%
 
16.391< 0.1%
 
16.21< 0.1%
 
15.981< 0.1%
 
15.971< 0.1%
 

account length
Real number (ℝ≥0)

Distinct count218
Unique (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2586
Minimum1.0
Maximum243.0
Zeros0
Zeros (%)0.0%
Memory size39.2 KiB
2020-08-25T01:14:10.680629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.69455955
Coefficient of variation (CV)0.3959217418
Kurtosis-0.1016210812
Mean100.2586
Median Absolute Deviation (MAD)27
Skewness0.1092911238
Sum501293
Variance1575.658058
2020-08-25T01:14:10.782249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
90651.3%
 
87591.2%
 
93571.1%
 
105571.1%
 
112561.1%
 
100551.1%
 
86551.1%
 
101551.1%
 
116541.1%
 
103541.1%
 
78531.1%
 
127531.1%
 
98531.1%
 
95531.1%
 
94521.0%
 
120521.0%
 
106511.0%
 
117511.0%
 
122511.0%
 
88501.0%
 
92501.0%
 
115481.0%
 
80470.9%
 
74470.9%
 
113460.9%
 
Other values (193)367673.5%
 
ValueCountFrequency (%) 
1110.2%
 
22< 0.1%
 
380.2%
 
430.1%
 
52< 0.1%
 
62< 0.1%
 
750.1%
 
82< 0.1%
 
930.1%
 
1030.1%
 
ValueCountFrequency (%) 
2431< 0.1%
 
2381< 0.1%
 
2331< 0.1%
 
2322< 0.1%
 
2252< 0.1%
 
2242< 0.1%
 
2222< 0.1%
 
2211< 0.1%
 
21730.1%
 
2161< 0.1%
 

total day calls
Real number (ℝ≥0)

Distinct count123
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0294
Minimum0.0
Maximum165.0
Zeros2
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:10.895176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.83119742
Coefficient of variation (CV)0.1982536876
Kurtosis0.1785677943
Mean100.0294
Median Absolute Deviation (MAD)13
Skewness-0.08489096367
Sum500147
Variance393.2763909
2020-08-25T01:14:11.015582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1051172.3%
 
1021132.3%
 
951082.2%
 
941042.1%
 
971042.1%
 
1001022.0%
 
1121012.0%
 
1101012.0%
 
1081002.0%
 
921002.0%
 
98992.0%
 
106992.0%
 
104982.0%
 
107982.0%
 
96982.0%
 
101951.9%
 
91951.9%
 
88931.9%
 
99911.8%
 
109891.8%
 
114891.8%
 
93861.7%
 
113851.7%
 
103841.7%
 
90831.7%
 
Other values (98)256851.4%
 
ValueCountFrequency (%) 
02< 0.1%
 
301< 0.1%
 
341< 0.1%
 
351< 0.1%
 
361< 0.1%
 
392< 0.1%
 
402< 0.1%
 
422< 0.1%
 
4440.1%
 
4530.1%
 
ValueCountFrequency (%) 
1651< 0.1%
 
1631< 0.1%
 
1602< 0.1%
 
15830.1%
 
1572< 0.1%
 
15630.1%
 
1522< 0.1%
 
15170.1%
 
15060.1%
 
1492< 0.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
3677
1
1323
ValueCountFrequency (%) 
0367773.5%
 
1132326.5%
 

total eve calls
Real number (ℝ≥0)

Distinct count126
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.191
Minimum0.0
Maximum170.0
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:11.131603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.82649583
Coefficient of variation (CV)0.1978869942
Kurtosis0.1173634027
Mean100.191
Median Absolute Deviation (MAD)13
Skewness-0.02017520328
Sum500955
Variance393.089937
2020-08-25T01:14:11.249215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1051152.3%
 
971102.2%
 
911102.2%
 
1031062.1%
 
941062.1%
 
1011042.1%
 
961002.0%
 
1041002.0%
 
109992.0%
 
102992.0%
 
98992.0%
 
88982.0%
 
108982.0%
 
89982.0%
 
110961.9%
 
106961.9%
 
111961.9%
 
100901.8%
 
93891.8%
 
107851.7%
 
95841.7%
 
90841.7%
 
115831.7%
 
92801.6%
 
83801.6%
 
Other values (101)259551.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
121< 0.1%
 
361< 0.1%
 
371< 0.1%
 
381< 0.1%
 
421< 0.1%
 
431< 0.1%
 
442< 0.1%
 
451< 0.1%
 
4650.1%
 
ValueCountFrequency (%) 
1701< 0.1%
 
1691< 0.1%
 
1681< 0.1%
 
1641< 0.1%
 
1591< 0.1%
 
1571< 0.1%
 
1561< 0.1%
 
15550.1%
 
15440.1%
 
1531< 0.1%
 

phone number
Real number (ℝ≥0)

UNIQUE

Distinct count5000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.5
Minimum0
Maximum4999
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:11.377781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile249.95
Q11249.75
median2499.5
Q33749.25
95-th percentile4749.05
Maximum4999
Range4999
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1443.520003
Coefficient of variation (CV)0.577523506
Kurtosis-1.2
Mean2499.5
Median Absolute Deviation (MAD)1250
Skewness0
Sum12497500
Variance2083750
2020-08-25T01:14:11.477896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
46551< 0.1%
 
5491< 0.1%
 
46471< 0.1%
 
26001< 0.1%
 
5531< 0.1%
 
46511< 0.1%
 
26041< 0.1%
 
5571< 0.1%
 
26081< 0.1%
 
46431< 0.1%
 
5611< 0.1%
 
46591< 0.1%
 
26121< 0.1%
 
5651< 0.1%
 
46631< 0.1%
 
26161< 0.1%
 
5691< 0.1%
 
25961< 0.1%
 
5451< 0.1%
 
24641< 0.1%
 
25801< 0.1%
 
46191< 0.1%
 
25721< 0.1%
 
5251< 0.1%
 
Other values (4975)497599.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
49991< 0.1%
 
49981< 0.1%
 
49971< 0.1%
 
49961< 0.1%
 
49951< 0.1%
 
49941< 0.1%
 
49931< 0.1%
 
49921< 0.1%
 
49911< 0.1%
 
49901< 0.1%
 

total intl charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count170
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7711959999999998
Minimum0.0
Maximum5.4
Zeros24
Zeros (%)0.5%
Memory size39.2 KiB
2020-08-25T01:14:11.588940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.7455137073
Coefficient of variation (CV)0.269022367
Kurtosis0.6559885458
Mean2.771196
Median Absolute Deviation (MAD)0.48
Skewness-0.2102861147
Sum13855.98
Variance0.5557906877
2020-08-25T01:14:11.692827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3901.8%
 
2.65881.8%
 
3.05831.7%
 
2.73811.6%
 
3.08811.6%
 
2.94801.6%
 
2.62791.6%
 
2.84781.6%
 
2.97781.6%
 
2.86781.6%
 
2.7781.6%
 
2.75771.5%
 
2.57751.5%
 
2.89751.5%
 
3.02721.4%
 
2.67721.4%
 
3.13721.4%
 
3.11711.4%
 
2.78701.4%
 
2.81691.4%
 
2.43681.4%
 
2.4671.3%
 
3.24671.3%
 
2.92671.3%
 
2.48661.3%
 
Other values (145)311862.4%
 
ValueCountFrequency (%) 
0240.5%
 
0.111< 0.1%
 
0.32< 0.1%
 
0.351< 0.1%
 
0.5430.1%
 
0.572< 0.1%
 
0.592< 0.1%
 
0.651< 0.1%
 
0.681< 0.1%
 
0.71< 0.1%
 
ValueCountFrequency (%) 
5.41< 0.1%
 
5.322< 0.1%
 
5.211< 0.1%
 
5.181< 0.1%
 
5.12< 0.1%
 
5.051< 0.1%
 
51< 0.1%
 
4.971< 0.1%
 
4.941< 0.1%
 
4.912< 0.1%
 

total intl minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count170
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.261779999999998
Minimum0.0
Maximum20.0
Zeros24
Zeros (%)0.5%
Memory size39.2 KiB
2020-08-25T01:14:11.809217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.761395715
Coefficient of variation (CV)0.2690951974
Kurtosis0.6553166102
Mean10.26178
Median Absolute Deviation (MAD)1.8
Skewness-0.2099662929
Sum51308.9
Variance7.625306293
2020-08-25T01:14:11.924373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11.1901.8%
 
9.8881.8%
 
11.3831.7%
 
10.1811.6%
 
11.4811.6%
 
10.9801.6%
 
9.7791.6%
 
11781.6%
 
10.6781.6%
 
10.5781.6%
 
10781.6%
 
10.2771.5%
 
9.5751.5%
 
10.7751.5%
 
11.6721.4%
 
11.2721.4%
 
9.9721.4%
 
11.5711.4%
 
10.3701.4%
 
10.4691.4%
 
9681.4%
 
10.8671.3%
 
8.9671.3%
 
12671.3%
 
9.2661.3%
 
Other values (145)311862.4%
 
ValueCountFrequency (%) 
0240.5%
 
0.41< 0.1%
 
1.12< 0.1%
 
1.31< 0.1%
 
230.1%
 
2.12< 0.1%
 
2.22< 0.1%
 
2.41< 0.1%
 
2.51< 0.1%
 
2.61< 0.1%
 
ValueCountFrequency (%) 
201< 0.1%
 
19.72< 0.1%
 
19.31< 0.1%
 
19.21< 0.1%
 
18.92< 0.1%
 
18.71< 0.1%
 
18.51< 0.1%
 
18.41< 0.1%
 
18.31< 0.1%
 
18.22< 0.1%
 

total night minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1853
Unique (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.39162000000002
Minimum0.0
Maximum395.0
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:12.063023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.395
Q1166.9
median200.4
Q3234.7
95-th percentile283.405
Maximum395
Range395
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation50.52778926
Coefficient of variation (CV)0.2521452207
Kurtosis0.08235919689
Mean200.39162
Median Absolute Deviation (MAD)33.8
Skewness0.01932491656
Sum1001958.1
Variance2553.057487
2020-08-25T01:14:12.175136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
186.2110.2%
 
188.2110.2%
 
194.3110.2%
 
208.9100.2%
 
228.1100.2%
 
214.6100.2%
 
197.490.2%
 
214.790.2%
 
192.790.2%
 
193.690.2%
 
21090.2%
 
191.490.2%
 
169.490.2%
 
221.680.2%
 
230.180.2%
 
181.280.2%
 
213.780.2%
 
214.580.2%
 
215.880.2%
 
214.480.2%
 
239.980.2%
 
182.380.2%
 
220.380.2%
 
221.780.2%
 
21480.2%
 
Other values (1828)477895.6%
 
ValueCountFrequency (%) 
01< 0.1%
 
23.21< 0.1%
 
43.71< 0.1%
 
451< 0.1%
 
46.71< 0.1%
 
47.41< 0.1%
 
50.12< 0.1%
 
50.91< 0.1%
 
53.31< 0.1%
 
541< 0.1%
 
ValueCountFrequency (%) 
3951< 0.1%
 
381.91< 0.1%
 
381.61< 0.1%
 
377.51< 0.1%
 
367.71< 0.1%
 
364.91< 0.1%
 
364.31< 0.1%
 
359.91< 0.1%
 
355.11< 0.1%
 
354.91< 0.1%
 

area code
Categorical

Distinct count3
Unique (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
415
2495
408
1259
510
1246
ValueCountFrequency (%) 
415249549.9%
 
408125925.2%
 
510124624.9%
 
2020-08-25T01:14:12.332987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0750530.0%
 
.500020.0%
 
4375415.0%
 
1374115.0%
 
5374115.0%
 
812595.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number2000080.0%
 
Other Punctuation500020.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0750537.5%
 
4375418.8%
 
1374118.7%
 
5374118.7%
 
812596.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.5000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common25000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0750530.0%
 
.500020.0%
 
4375415.0%
 
1374115.0%
 
5374115.0%
 
812595.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII25000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0750530.0%
 
.500020.0%
 
4375415.0%
 
1374115.0%
 
5374115.0%
 
812595.0%
 

total day charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1961
Unique (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.649668
Minimum0.0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:12.441684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.59
Q124.43
median30.62
Q336.75
95-th percentile46.0905
Maximum59.76
Range59.76
Interquartile range (IQR)12.32

Descriptive statistics

Standard deviation9.162068692
Coefficient of variation (CV)0.298928807
Kurtosis-0.02116592527
Mean30.649668
Median Absolute Deviation (MAD)6.17
Skewness-0.01172900707
Sum153248.34
Variance83.94350271
2020-08-25T01:14:12.549568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
26.18100.2%
 
32.18100.2%
 
30.690.2%
 
30.1190.2%
 
29.6790.2%
 
27.1290.2%
 
31.3790.2%
 
32.2780.2%
 
28.6680.2%
 
30.9680.2%
 
31.4580.2%
 
24.4380.2%
 
36.6580.2%
 
28.6380.2%
 
31.1880.2%
 
27.2570.1%
 
36.9270.1%
 
35.6870.1%
 
32.8670.1%
 
37.5770.1%
 
37.6470.1%
 
30.1270.1%
 
33.3270.1%
 
24.0270.1%
 
32.9570.1%
 
Other values (1936)480196.0%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.441< 0.1%
 
1.121< 0.1%
 
1.221< 0.1%
 
1.331< 0.1%
 
1.341< 0.1%
 
2.131< 0.1%
 
2.991< 0.1%
 
3.211< 0.1%
 
3.321< 0.1%
 
ValueCountFrequency (%) 
59.761< 0.1%
 
59.641< 0.1%
 
58.961< 0.1%
 
58.71< 0.1%
 
57.531< 0.1%
 
57.361< 0.1%
 
57.041< 0.1%
 
56.831< 0.1%
 
56.591< 0.1%
 
56.461< 0.1%
 

number customer service calls
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5704
Minimum0.0
Maximum9.0
Zeros1023
Zeros (%)20.5%
Memory size39.2 KiB
2020-08-25T01:14:12.659122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.306363333
Coefficient of variation (CV)0.8318666153
Kurtosis1.48109554
Mean1.5704
Median Absolute Deviation (MAD)1
Skewness1.04246233
Sum7852
Variance1.706585157
2020-08-25T01:14:12.765969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1178635.7%
 
2112722.5%
 
0102320.5%
 
366513.3%
 
42525.0%
 
5961.9%
 
6340.7%
 
7130.3%
 
82< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
0102320.5%
 
1178635.7%
 
2112722.5%
 
366513.3%
 
42525.0%
 
5961.9%
 
6340.7%
 
7130.3%
 
82< 0.1%
 
92< 0.1%
 
ValueCountFrequency (%) 
92< 0.1%
 
82< 0.1%
 
7130.3%
 
6340.7%
 
5961.9%
 
42525.0%
 
366513.3%
 
2112722.5%
 
1178635.7%
 
0102320.5%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
4527
1
 
473
ValueCountFrequency (%) 
0452790.5%
 
14739.5%
 

total eve charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1659
Unique (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.054322
Minimum0.0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:13.048089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.0695
Q114.14
median17.09
Q319.9
95-th percentile24.112
Maximum30.91
Range30.91
Interquartile range (IQR)5.76

Descriptive statistics

Standard deviation4.296843301
Coefficient of variation (CV)0.251950403
Kurtosis0.0512887853
Mean17.054322
Median Absolute Deviation (MAD)2.89
Skewness-0.01099032836
Sum85271.61
Variance18.46286235
2020-08-25T01:14:13.159690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
14.25150.3%
 
15.9150.3%
 
16.12140.3%
 
18.79130.3%
 
18.96130.3%
 
16.97130.3%
 
19.41120.2%
 
16.8110.2%
 
17.09110.2%
 
16.18110.2%
 
17.99110.2%
 
18.62110.2%
 
16.35110.2%
 
16.41110.2%
 
17.82110.2%
 
16.63100.2%
 
17.6100.2%
 
14.44100.2%
 
14.2100.2%
 
17.43100.2%
 
16.24100.2%
 
19.63100.2%
 
17.77100.2%
 
18.33100.2%
 
20.2100.2%
 
Other values (1634)471794.3%
 
ValueCountFrequency (%) 
01< 0.1%
 
1.91< 0.1%
 
2.651< 0.1%
 
3.211< 0.1%
 
3.541< 0.1%
 
3.591< 0.1%
 
3.611< 0.1%
 
3.731< 0.1%
 
4.022< 0.1%
 
4.091< 0.1%
 
ValueCountFrequency (%) 
30.911< 0.1%
 
30.751< 0.1%
 
30.541< 0.1%
 
30.111< 0.1%
 
29.931< 0.1%
 
29.891< 0.1%
 
29.831< 0.1%
 
29.791< 0.1%
 
29.71< 0.1%
 
29.661< 0.1%
 

total day minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1961
Unique (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2889
Minimum0.0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:13.269648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.7
Q1143.7
median180.1
Q3216.2
95-th percentile271.105
Maximum351.5
Range351.5
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation53.89469917
Coefficient of variation (CV)0.2989352044
Kurtosis-0.02129447073
Mean180.2889
Median Absolute Deviation (MAD)36.3
Skewness-0.01173082717
Sum901444.5
Variance2904.638599
2020-08-25T01:14:13.388826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
189.3100.2%
 
154100.2%
 
18090.2%
 
174.590.2%
 
177.190.2%
 
184.590.2%
 
159.590.2%
 
168.680.2%
 
183.480.2%
 
143.780.2%
 
168.480.2%
 
182.180.2%
 
215.680.2%
 
18580.2%
 
189.880.2%
 
19670.1%
 
19770.1%
 
157.170.1%
 
191.370.1%
 
193.370.1%
 
138.170.1%
 
217.270.1%
 
162.370.1%
 
209.970.1%
 
21670.1%
 
Other values (1936)480196.0%
 
ValueCountFrequency (%) 
02< 0.1%
 
2.61< 0.1%
 
6.61< 0.1%
 
7.21< 0.1%
 
7.81< 0.1%
 
7.91< 0.1%
 
12.51< 0.1%
 
17.61< 0.1%
 
18.91< 0.1%
 
19.51< 0.1%
 
ValueCountFrequency (%) 
351.51< 0.1%
 
350.81< 0.1%
 
346.81< 0.1%
 
345.31< 0.1%
 
338.41< 0.1%
 
337.41< 0.1%
 
335.51< 0.1%
 
334.31< 0.1%
 
332.91< 0.1%
 
332.11< 0.1%
 

total eve minutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1879
Unique (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.63656
Minimum0.0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:13.515784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.495
Q1166.375
median201
Q3234.1
95-th percentile283.72
Maximum363.7
Range363.7
Interquartile range (IQR)67.725

Descriptive statistics

Standard deviation50.55130897
Coefficient of variation (CV)0.2519546237
Kurtosis0.05137513056
Mean200.63656
Median Absolute Deviation (MAD)34
Skewness-0.01101769459
Sum1003182.8
Variance2555.434838
2020-08-25T01:14:13.629267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
199.7100.2%
 
169.9100.2%
 
230.9100.2%
 
210.690.2%
 
18790.2%
 
216.590.2%
 
223.590.2%
 
188.890.2%
 
19490.2%
 
161.790.2%
 
187.590.2%
 
167.690.2%
 
209.480.2%
 
18280.2%
 
23080.2%
 
170.980.2%
 
211.780.2%
 
211.580.2%
 
22380.2%
 
19680.2%
 
177.880.2%
 
228.480.2%
 
195.780.2%
 
20180.2%
 
221.180.2%
 
Other values (1854)478595.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
22.31< 0.1%
 
31.21< 0.1%
 
37.81< 0.1%
 
41.71< 0.1%
 
42.21< 0.1%
 
42.51< 0.1%
 
43.91< 0.1%
 
47.32< 0.1%
 
48.11< 0.1%
 
ValueCountFrequency (%) 
363.71< 0.1%
 
361.81< 0.1%
 
359.31< 0.1%
 
354.21< 0.1%
 
352.11< 0.1%
 
351.61< 0.1%
 
350.91< 0.1%
 
350.51< 0.1%
 
349.41< 0.1%
 
348.91< 0.1%
 

total night calls
Real number (ℝ≥0)

Distinct count131
Unique (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.9192
Minimum0.0
Maximum175.0
Zeros1
Zeros (%)< 0.1%
Memory size39.2 KiB
2020-08-25T01:14:13.761661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.95868586
Coefficient of variation (CV)0.1997482552
Kurtosis0.1444380753
Mean99.9192
Median Absolute Deviation (MAD)13
Skewness0.002132842744
Sum499596
Variance398.3491412
2020-08-25T01:14:13.889791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1051212.4%
 
1021092.2%
 
1001082.2%
 
1041062.1%
 
991052.1%
 
1031042.1%
 
941032.1%
 
911032.1%
 
981022.0%
 
951022.0%
 
109971.9%
 
96951.9%
 
106921.8%
 
108911.8%
 
97901.8%
 
92891.8%
 
90891.8%
 
110881.8%
 
113861.7%
 
112861.7%
 
89861.7%
 
93841.7%
 
107831.7%
 
87811.6%
 
111771.5%
 
Other values (106)262352.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
121< 0.1%
 
331< 0.1%
 
361< 0.1%
 
382< 0.1%
 
401< 0.1%
 
411< 0.1%
 
4240.1%
 
431< 0.1%
 
441< 0.1%
 
ValueCountFrequency (%) 
1751< 0.1%
 
1701< 0.1%
 
1681< 0.1%
 
1661< 0.1%
 
1651< 0.1%
 
1641< 0.1%
 
1611< 0.1%
 
1601< 0.1%
 
1592< 0.1%
 
1582< 0.1%
 

target
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
4293
1
 
707
ValueCountFrequency (%) 
0429385.9%
 
170714.1%
 

Interactions

2020-08-25T01:13:28.820252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:28.955992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.107849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.254061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.397027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.535210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.684791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.821409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:29.956056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.104024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.254340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.397611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.528784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.670098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.812306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:30.968821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:31.127547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:31.281255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:31.445526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:31.608180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:31.961581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.119361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.283763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.438196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.593430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.764292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:32.929857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.085954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.237597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.391548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.549305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.707363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:33.864942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.004514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.165684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.318969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.460955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.605659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.761812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:34.902771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.043722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.201238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.355513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.507814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.652837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.804789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:35.956602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:36.116279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:36.280372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:36.585804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:36.728857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:36.875502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.009487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.147979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.290269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.422158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.557571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.702083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.854668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:37.989224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:38.117101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:13:38.255815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:14:08.551138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:08.706851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:14:15.165804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:14:09.216014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:09.730158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

statetotal intl callstotal night chargeaccount lengthtotal day callsvoice mail plantotal eve callsphone numbertotal intl chargetotal intl minutestotal night minutesarea codetotal day chargenumber customer service callsinternational plantotal eve chargetotal day minutestotal eve minutestotal night callstarget
0163.011.01128.0110.0199.028452.7010.0244.7415.045.071.0016.78265.1197.491.00
1353.011.45107.0123.01103.023013.7013.7254.4415.027.471.0016.62161.6195.5103.00
2315.07.32137.0114.00110.016163.2912.2162.6415.041.380.0010.30243.4121.2104.00
3357.08.8684.071.0088.025101.786.6196.9408.050.902.015.26299.461.989.00
4363.08.4175.0113.00122.01552.7310.1186.9415.028.343.0112.61166.7148.3121.00
516.09.18118.098.00101.033551.706.3203.9510.037.980.0118.75223.4220.6118.00
6197.09.57121.088.01108.015162.037.5212.6510.037.093.0029.62218.2348.5118.00
7246.09.53147.079.0094.01161.927.1211.8415.026.690.018.76157.0103.196.00
8184.09.71117.097.0080.04252.358.7215.8408.031.371.0029.89184.5351.690.00
9495.014.69141.084.01111.01633.0211.2326.4415.043.960.0118.87258.6222.097.00

Last rows

statetotal intl callstotal night chargeaccount lengthtotal day callsvoice mail plantotal eve callsphone numbertotal intl chargetotal intl minutestotal night minutesarea codetotal day chargenumber customer service callsinternational plantotal eve chargetotal day minutestotal eve minutestotal night callstarget
4990286.010.41140.0115.00101.019492.037.5231.3510.041.601.0021.98244.7258.6112.01
499135.011.5497.089.0091.043672.388.8256.5510.042.941.0028.93252.6340.367.01
4992266.09.6283.070.0088.014762.7810.3213.7415.032.010.0020.72188.3243.879.00
4993496.08.3873.089.0082.044673.1111.5186.2408.030.243.0011.15177.9131.289.00
4994277.05.8175.0101.00126.047261.866.9129.1408.029.021.0016.41170.7193.1104.00
4995115.013.3950.0127.01126.020002.679.9297.5408.040.072.0018.96235.7223.0116.00
4996492.09.61152.090.0073.03943.9714.7213.6415.031.313.0021.83184.2256.8113.01
499774.09.5661.089.00128.03133.6713.6212.4415.023.901.0014.69140.6172.897.00
499876.010.10109.067.0092.034712.308.5224.4510.032.100.0014.59188.8171.789.00
49994616.06.9786.0102.01104.024122.519.3154.8415.022.000.0022.70129.4267.1100.00