We analyze multiple affecting factors like time and rankings to forecast sales using Vector Auto-regression model in Python
email: dhiraj.tripathi@rutgers.edu , contact : - 551-358-9117
In this data challenge, we are given two different datasets as 'ranks.csv' and 'sales.csv' . Our primary goal is to analyze the tend between the ranks and sales. The thought process here is to estimate sales of an application by considering ranks as a function. We will feed in some data to our machine learning model and forecast the sales value 3 months ahead.
To achieve this, we will start by importing some required packages for our script as below:
import pandas as pdLet's read the two csv files given to us and have a quick look at the dimensions and data types of the columns from these files.
import numpy as np
rankdata = pd.read_csv("F:/GIT/Appfigues/ranks.csv" , index_col = False)
salesdata = pd.read_csv("F:/GIT/Appfigues/sales.csv", index_col = False)rankdata.head()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| Date | app_id | r0 | r1 | r2 | r3 | r4 | r5 | r6 | r7 | ... | s1 | s2 | s3 | s4 | s5 | cs1 | cs2 | cs3 | cs4 | cs5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 | 1 | 622.0 | 622.0 | 621.0 | 619.0 | 621.0 | 561.0 | 562.0 | 563.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 47.0 | 8.0 | 6.0 | 12.0 | 30.0 |
| 1 | 2016-01-01 | 2 | 574.0 | 574.0 | 573.0 | 571.0 | 573.0 | 543.0 | 544.0 | 545.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 142.0 | 88.0 | 296.0 | 826.0 | 3177.0 |
| 2 | 2016-01-01 | 4 | 144.0 | 144.0 | 144.0 | 144.0 | 144.0 | 144.0 | 143.0 | 142.0 | ... | 0.0 | 0.0 | 0.0 | 2.0 | 4.0 | 552.0 | 321.0 | 705.0 | 1409.0 | 4417.0 |
| 3 | 2016-01-01 | 5 | 919.0 | 919.0 | 918.0 | 914.0 | 917.0 | 875.0 | 874.0 | 877.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 226.0 | 95.0 | 322.0 | 1121.0 | 4013.0 |
| 4 | 2016-01-01 | 8 | 903.0 | 903.0 | 902.0 | 898.0 | 901.0 | 920.0 | 919.0 | 922.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 356.0 | 181.0 | 362.0 | 1744.0 | 6884.0 |
5 rows × 36 columns
salesdata.head()
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| Date | app_id | sales | |
|---|---|---|---|
| 0 | 2016-01-01 | 320 | 2412 |
| 1 | 2016-01-01 | 406 | 1308 |
| 2 | 2016-01-01 | 459 | 2037 |
| 3 | 2016-01-01 | 722 | 2052 |
| 4 | 2016-01-01 | 1234 | 1553 |
rankdata.ndim2
rankdata.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 97608 entries, 0 to 97607
Data columns (total 36 columns):
Date 97608 non-null object
app_id 97608 non-null int64
r0 89992 non-null float64
r1 89953 non-null float64
r2 88974 non-null float64
r3 89999 non-null float64
r4 89997 non-null float64
r5 89886 non-null float64
r6 89888 non-null float64
r7 89991 non-null float64
r8 89994 non-null float64
r9 89995 non-null float64
r10 89977 non-null float64
r11 89997 non-null float64
r12 89958 non-null float64
r13 89931 non-null float64
r14 89990 non-null float64
r15 89998 non-null float64
r16 89960 non-null float64
r17 89984 non-null float64
r18 89991 non-null float64
r19 89973 non-null float64
r20 89993 non-null float64
r21 89999 non-null float64
r22 89995 non-null float64
r23 89981 non-null float64
s1 93967 non-null float64
s2 93967 non-null float64
s3 93967 non-null float64
s4 93967 non-null float64
s5 93967 non-null float64
cs1 93967 non-null float64
cs2 93967 non-null float64
cs3 93967 non-null float64
cs4 93967 non-null float64
cs5 93967 non-null float64
dtypes: float64(34), int64(1), object(1)
memory usage: 26.8+ MB
rankdata.shape(97608, 36)
salesdata.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1105 entries, 0 to 1104
Data columns (total 3 columns):
Date 1105 non-null object
app_id 1105 non-null int64
sales 1105 non-null int64
dtypes: int64(2), object(1)
memory usage: 26.0+ KB
salesdata.shape(1105, 3)
We can notice that our data sets are sufficiently large in terms of number of rows * columns and also there are some matching columns in both the datasets such as Date , app_id .
The first step towards creating a model is to merge both the datasets and extract only the relevant information from both. For this, we will use the merge function in python as if we are using an inner join in sql.
subsetDataFrame = pd.merge(rankdata, salesdata, how='inner', on=['Date', 'app_id'])print(subsetDataFrame) Date app_id r0 r1 r2 r3 r4 r5 r6 \
0 2016-01-01 320 488.0 488.0 488.0 486.0 487.0 475.0 476.0
1 2016-01-01 406 685.0 685.0 684.0 681.0 684.0 702.0 701.0
2 2016-01-01 459 597.0 597.0 596.0 594.0 596.0 603.0 604.0
3 2016-01-01 722 532.0 532.0 532.0 530.0 531.0 547.0 548.0
4 2016-01-01 1234 813.0 813.0 812.0 809.0 812.0 831.0 830.0
5 2016-01-01 1490 528.0 528.0 528.0 526.0 527.0 519.0 520.0
6 2016-01-01 2398 NaN NaN NaN NaN NaN NaN NaN
7 2016-01-01 2891 5.0 5.0 5.0 5.0 5.0 5.0 6.0
8 2016-01-02 320 438.0 435.0 446.0 445.0 446.0 444.0 446.0
9 2016-01-02 406 738.0 735.0 747.0 748.0 749.0 746.0 749.0
10 2016-01-02 459 610.0 607.0 582.0 581.0 582.0 580.0 582.0
11 2016-01-02 722 601.0 598.0 603.0 603.0 604.0 602.0 604.0
12 2016-01-02 1234 678.0 675.0 698.0 698.0 699.0 696.0 699.0
13 2016-01-02 1490 535.0 532.0 564.0 563.0 564.0 562.0 564.0
14 2016-01-02 2346 NaN NaN NaN NaN NaN NaN NaN
15 2016-01-02 2398 950.0 947.0 969.0 970.0 972.0 967.0 972.0
16 2016-01-02 2891 6.0 6.0 6.0 6.0 6.0 6.0 6.0
17 2016-01-03 320 492.0 492.0 492.0 491.0 491.0 492.0 504.0
18 2016-01-03 406 740.0 737.0 740.0 738.0 739.0 740.0 728.0
19 2016-01-03 459 558.0 558.0 558.0 557.0 557.0 558.0 568.0
20 2016-01-03 722 647.0 646.0 647.0 645.0 646.0 647.0 679.0
21 2016-01-03 1234 809.0 806.0 809.0 807.0 808.0 809.0 836.0
22 2016-01-03 1490 533.0 533.0 533.0 532.0 532.0 533.0 564.0
23 2016-01-03 2346 821.0 818.0 821.0 819.0 820.0 821.0 702.0
24 2016-01-03 2891 5.0 5.0 5.0 5.0 5.0 5.0 5.0
25 2016-01-04 320 521.0 521.0 521.0 521.0 521.0 521.0 521.0
26 2016-01-04 406 816.0 816.0 816.0 816.0 816.0 816.0 816.0
27 2016-01-04 459 576.0 576.0 576.0 576.0 576.0 576.0 576.0
28 2016-01-04 722 707.0 707.0 707.0 707.0 707.0 707.0 707.0
29 2016-01-04 1234 844.0 844.0 844.0 844.0 844.0 844.0 844.0
... ... ... ... ... ... ... ... ... ...
1075 2016-03-30 1264 NaN NaN NaN NaN NaN NaN NaN
1076 2016-03-30 1461 960.0 967.0 967.0 967.0 967.0 967.0 967.0
1077 2016-03-30 1490 317.0 324.0 324.0 324.0 324.0 324.0 324.0
1078 2016-03-30 1874 584.0 591.0 591.0 591.0 591.0 591.0 591.0
1079 2016-03-30 2346 307.0 314.0 314.0 314.0 314.0 314.0 314.0
1080 2016-03-30 2373 969.0 976.0 976.0 976.0 976.0 976.0 976.0
1081 2016-03-30 2398 856.0 863.0 863.0 863.0 863.0 863.0 863.0
1082 2016-03-30 2667 460.0 467.0 467.0 467.0 467.0 467.0 467.0
1083 2016-03-30 2891 17.0 18.0 18.0 18.0 18.0 18.0 18.0
1084 2016-03-30 3308 NaN NaN NaN NaN NaN NaN NaN
1085 2016-03-30 3373 252.0 259.0 259.0 259.0 259.0 259.0 259.0
1086 2016-03-30 3428 568.0 575.0 575.0 575.0 575.0 575.0 575.0
1087 2016-03-30 3550 537.0 544.0 544.0 544.0 544.0 544.0 544.0
1088 2016-03-31 320 444.0 445.0 445.0 445.0 445.0 445.0 445.0
1089 2016-03-31 459 581.0 498.0 498.0 498.0 498.0 498.0 498.0
1090 2016-03-31 676 647.0 633.0 633.0 633.0 633.0 633.0 633.0
1091 2016-03-31 722 625.0 658.0 658.0 658.0 658.0 658.0 658.0
1092 2016-03-31 1234 517.0 512.0 512.0 512.0 512.0 512.0 512.0
1093 2016-03-31 1264 989.0 977.0 977.0 977.0 977.0 977.0 977.0
1094 2016-03-31 1461 882.0 884.0 884.0 884.0 884.0 884.0 884.0
1095 2016-03-31 1490 547.0 587.0 587.0 587.0 587.0 587.0 587.0
1096 2016-03-31 1874 545.0 571.0 571.0 571.0 571.0 571.0 571.0
1097 2016-03-31 2346 362.0 377.0 377.0 377.0 377.0 377.0 377.0
1098 2016-03-31 2398 935.0 916.0 916.0 916.0 916.0 916.0 916.0
1099 2016-03-31 2667 475.0 488.0 488.0 488.0 488.0 488.0 488.0
1100 2016-03-31 2891 20.0 21.0 21.0 21.0 21.0 21.0 21.0
1101 2016-03-31 3308 964.0 904.0 904.0 904.0 904.0 904.0 904.0
1102 2016-03-31 3373 282.0 310.0 310.0 310.0 310.0 310.0 310.0
1103 2016-03-31 3428 554.0 573.0 573.0 573.0 573.0 573.0 573.0
1104 2016-03-31 3550 274.0 217.0 217.0 217.0 217.0 217.0 217.0
r7 ... s2 s3 s4 s5 cs1 cs2 cs3 cs4 \
0 477.0 ... 0.0 2.0 0.0 0.0 746.0 284.0 290.0 568.0
1 704.0 ... 0.0 0.0 0.0 0.0 1376.0 1008.0 5674.0 20010.0
2 605.0 ... 0.0 0.0 0.0 1.0 10.0 2.0 14.0 32.0
3 549.0 ... 0.0 2.0 8.0 1.0 4025.0 1129.0 1488.0 2007.0
4 833.0 ... 0.0 0.0 0.0 0.0 332.0 163.0 269.0 395.0
5 521.0 ... 0.0 0.0 0.0 2.0 248.0 91.0 167.0 290.0
6 NaN ... 0.0 0.0 0.0 0.0 40.0 13.0 17.0 24.0
7 6.0 ... 4.0 8.0 16.0 106.0 73.0 41.0 71.0 191.0
8 446.0 ... 0.0 0.0 0.0 0.0 746.0 284.0 290.0 568.0
9 749.0 ... 0.0 0.0 0.0 0.0 1376.0 1008.0 5674.0 20010.0
10 582.0 ... 0.0 0.0 0.0 1.0 11.0 2.0 14.0 32.0
11 604.0 ... 0.0 0.0 2.0 2.0 4027.0 1129.0 1488.0 2009.0
12 699.0 ... 0.0 0.0 0.0 0.0 332.0 163.0 269.0 395.0
13 564.0 ... 0.0 2.0 0.0 0.0 248.0 91.0 169.0 290.0
14 NaN ... 0.0 0.0 0.0 1.0 21.0 30.0 51.0 74.0
15 972.0 ... 0.0 0.0 0.0 0.0 40.0 13.0 17.0 24.0
16 6.0 ... 0.0 16.0 14.0 111.0 79.0 41.0 87.0 205.0
17 503.0 ... 0.0 0.0 0.0 0.0 746.0 284.0 290.0 568.0
18 726.0 ... 0.0 0.0 0.0 0.0 1376.0 1008.0 5674.0 20010.0
19 566.0 ... 0.0 0.0 0.0 0.0 12.0 2.0 14.0 32.0
20 677.0 ... 2.0 0.0 0.0 0.0 4031.0 1131.0 1488.0 2009.0
21 834.0 ... 0.0 0.0 0.0 0.0 332.0 163.0 269.0 395.0
22 562.0 ... 0.0 0.0 0.0 0.0 250.0 91.0 169.0 290.0
23 700.0 ... 0.0 0.0 0.0 0.0 21.0 30.0 51.0 74.0
24 5.0 ... 4.0 6.0 26.0 61.0 85.0 45.0 93.0 231.0
25 530.0 ... 0.0 0.0 0.0 0.0 747.0 284.0 290.0 568.0
26 832.0 ... 0.0 0.0 0.0 0.0 1376.0 1008.0 5674.0 20010.0
27 568.0 ... 0.0 0.0 0.0 0.0 12.0 2.0 14.0 32.0
28 726.0 ... 0.0 0.0 0.0 2.0 4031.0 1131.0 1488.0 2009.0
29 856.0 ... 0.0 0.0 0.0 0.0 332.0 163.0 269.0 395.0
... ... ... ... ... ... ... ... ... ... ...
1075 NaN ... 0.0 0.0 0.0 0.0 994.0 433.0 462.0 339.0
1076 975.0 ... 0.0 0.0 0.0 2.0 102.0 85.0 243.0 1104.0
1077 386.0 ... 0.0 1.0 2.0 4.0 282.0 108.0 202.0 341.0
1078 523.0 ... 0.0 0.0 0.0 0.0 11.0 0.0 1.0 1.0
1079 335.0 ... 0.0 0.0 1.0 0.0 37.0 40.0 70.0 108.0
1080 939.0 ... 0.0 0.0 1.0 0.0 53.0 46.0 81.0 402.0
1081 896.0 ... 0.0 0.0 0.0 0.0 45.0 17.0 22.0 28.0
1082 446.0 ... 0.0 4.0 2.0 1.0 154.0 104.0 157.0 221.0
1083 18.0 ... 1.0 2.0 1.0 19.0 216.0 129.0 275.0 584.0
1084 998.0 ... 0.0 0.0 0.0 0.0 3.0 1.0 9.0 8.0
1085 272.0 ... 1.0 1.0 0.0 2.0 5.0 2.0 6.0 26.0
1086 643.0 ... NaN NaN NaN NaN NaN NaN NaN NaN
1087 537.0 ... NaN NaN NaN NaN NaN NaN NaN NaN
1088 445.0 ... 0.0 0.0 0.0 0.0 757.0 291.0 294.0 571.0
1089 509.0 ... 0.0 0.0 0.0 0.0 20.0 5.0 17.0 36.0
1090 619.0 ... 0.0 0.0 0.0 0.0 222.0 120.0 749.0 2727.0
1091 679.0 ... 0.0 0.0 0.0 2.0 4245.0 1175.0 1540.0 2121.0
1092 497.0 ... 0.0 0.0 0.0 0.0 344.0 169.0 270.0 404.0
1093 947.0 ... 0.0 1.0 1.0 1.0 995.0 433.0 463.0 340.0
1094 913.0 ... 0.0 0.0 0.0 2.0 102.0 85.0 243.0 1104.0
1095 639.0 ... 0.0 0.0 1.0 1.0 284.0 108.0 202.0 342.0
1096 583.0 ... 0.0 0.0 0.0 0.0 11.0 0.0 1.0 1.0
1097 394.0 ... 3.0 3.0 2.0 0.0 39.0 43.0 73.0 110.0
1098 919.0 ... 0.0 0.0 0.0 0.0 45.0 17.0 22.0 28.0
1099 476.0 ... 1.0 0.0 4.0 2.0 155.0 105.0 157.0 225.0
1100 22.0 ... 1.0 2.0 7.0 21.0 219.0 130.0 277.0 591.0
1101 959.0 ... 0.0 0.0 0.0 1.0 3.0 1.0 9.0 8.0
1102 314.0 ... 1.0 0.0 5.0 7.0 5.0 3.0 6.0 31.0
1103 656.0 ... NaN NaN NaN NaN NaN NaN NaN NaN
1104 195.0 ... NaN NaN NaN NaN NaN NaN NaN NaN
cs5 sales
0 1736.0 2412
1 82635.0 1308
2 264.0 2037
3 5477.0 2052
4 1320.0 1553
5 877.0 2152
6 120.0 1501
7 1036.0 89289
8 1736.0 1622
9 82635.0 1110
10 265.0 1553
11 5479.0 1467
12 1320.0 1011
13 877.0 1722
14 104.0 2165
15 120.0 1113
16 1147.0 84915
17 1736.0 1564
18 82635.0 963
19 265.0 1409
20 5479.0 1371
21 1320.0 938
22 877.0 1662
23 104.0 2029
24 1208.0 61345
25 1736.0 1609
26 82635.0 1168
27 265.0 1732
28 5481.0 1421
29 1320.0 989
... ... ...
1075 1061.0 2654
1076 4328.0 1039
1077 1029.0 1117
1078 11.0 1364
1079 127.0 2926
1080 2462.0 757
1081 122.0 1297
1082 345.0 3257
1083 2411.0 36890
1084 48.0 856
1085 93.0 3859
1086 NaN 1135
1087 NaN 6737
1088 1742.0 2041
1089 323.0 1526
1090 12217.0 1157
1091 5742.0 1388
1092 1331.0 1800
1093 1062.0 2433
1094 4330.0 1022
1095 1030.0 1223
1096 11.0 237
1097 127.0 11099
1098 122.0 1372
1099 347.0 3294
1100 2432.0 35903
1101 49.0 777
1102 100.0 3770
1103 NaN 1095
1104 NaN 7912
[1105 rows x 37 columns]
As we can see in the above dataframe , there are a lot of NaN values , which can distract our ML model. For the sake of simplicity , we will drop the records which have missing values in it. We can also impute the missing values, if required. But, our dataframe is large enough to train the model, even if we drop the records.
subsetDataFrame.dropna(inplace=True)subsetDataFrame.shape(919, 37)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 20)
display(subsetDataFrame)
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| Date | app_id | r0 | r1 | r2 | r3 | r4 | r5 | r6 | r7 | r8 | r9 | r10 | r11 | r12 | r13 | r14 | r15 | r16 | r17 | r18 | r19 | r20 | r21 | r22 | r23 | s1 | s2 | s3 | s4 | s5 | cs1 | cs2 | cs3 | cs4 | cs5 | sales | avgrank | avgrating | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-01-01 | 320 | 488.0 | 488.0 | 488.0 | 486.0 | 487.0 | 475.0 | 476.0 | 477.0 | 478.0 | 477.0 | 478.0 | 478.0 | 478.0 | 472.0 | 472.0 | 472.0 | 472.0 | 452.0 | 452.0 | 452.0 | 452.0 | 452.0 | 452.0 | 438.0 | 1.0 | 0.0 | 2.0 | 0.0 | 0.0 | 746.0 | 284.0 | 290.0 | 568.0 | 1736.0 | 2412 | 469.739130 | 0.50 |
| 1 | 2016-01-01 | 406 | 685.0 | 685.0 | 684.0 | 681.0 | 684.0 | 702.0 | 701.0 | 704.0 | 705.0 | 703.0 | 717.0 | 717.0 | 717.0 | 738.0 | 738.0 | 738.0 | 738.0 | 742.0 | 742.0 | 742.0 | 742.0 | 741.0 | 742.0 | 738.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1376.0 | 1008.0 | 5674.0 | 20010.0 | 82635.0 | 1308 | 719.173913 | 0.00 |
| 2 | 2016-01-01 | 459 | 597.0 | 597.0 | 596.0 | 594.0 | 596.0 | 603.0 | 604.0 | 605.0 | 606.0 | 604.0 | 618.0 | 618.0 | 618.0 | 607.0 | 607.0 | 607.0 | 607.0 | 598.0 | 598.0 | 598.0 | 598.0 | 598.0 | 598.0 | 610.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 10.0 | 2.0 | 14.0 | 32.0 | 264.0 | 2037 | 603.695652 | 0.25 |
| 3 | 2016-01-01 | 722 | 532.0 | 532.0 | 532.0 | 530.0 | 531.0 | 547.0 | 548.0 | 549.0 | 550.0 | 548.0 | 574.0 | 574.0 | 574.0 | 577.0 | 577.0 | 577.0 | 577.0 | 594.0 | 594.0 | 594.0 | 594.0 | 594.0 | 594.0 | 601.0 | 1.0 | 0.0 | 2.0 | 8.0 | 1.0 | 4025.0 | 1129.0 | 1488.0 | 2007.0 | 5477.0 | 2052 | 567.913043 | 2.75 |
| 4 | 2016-01-01 | 1234 | 813.0 | 813.0 | 812.0 | 809.0 | 812.0 | 831.0 | 830.0 | 833.0 | 834.0 | 832.0 | 802.0 | 802.0 | 802.0 | 775.0 | 775.0 | 775.0 | 775.0 | 713.0 | 713.0 | 713.0 | 713.0 | 712.0 | 713.0 | 678.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 332.0 | 163.0 | 269.0 | 395.0 | 1320.0 | 1553 | 776.826087 | 0.00 |
| 5 | 2016-01-01 | 1490 | 528.0 | 528.0 | 528.0 | 526.0 | 527.0 | 519.0 | 520.0 | 521.0 | 522.0 | 520.0 | 506.0 | 506.0 | 506.0 | 506.0 | 506.0 | 506.0 | 506.0 | 522.0 | 522.0 | 522.0 | 522.0 | 522.0 | 522.0 | 535.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 248.0 | 91.0 | 167.0 | 290.0 | 877.0 | 2152 | 518.260870 | 0.50 |
| 7 | 2016-01-01 | 2891 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 | 8.0 | 4.0 | 8.0 | 16.0 | 106.0 | 73.0 | 41.0 | 71.0 | 191.0 | 1036.0 | 89289 | 5.782609 | 33.50 |
| 8 | 2016-01-02 | 320 | 438.0 | 435.0 | 446.0 | 445.0 | 446.0 | 444.0 | 446.0 | 446.0 | 453.0 | 453.0 | 452.0 | 455.0 | 455.0 | 454.0 | 470.0 | 470.0 | 484.0 | 484.0 | 484.0 | 484.0 | 492.0 | 493.0 | 490.0 | 492.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 746.0 | 284.0 | 290.0 | 568.0 | 1736.0 | 1622 | 464.043478 | 0.00 |
| 9 | 2016-01-02 | 406 | 738.0 | 735.0 | 747.0 | 748.0 | 749.0 | 746.0 | 749.0 | 749.0 | 761.0 | 761.0 | 760.0 | 766.0 | 766.0 | 765.0 | 779.0 | 780.0 | 754.0 | 756.0 | 756.0 | 756.0 | 745.0 | 746.0 | 743.0 | 740.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1376.0 | 1008.0 | 5674.0 | 20010.0 | 82635.0 | 1110 | 754.652174 | 0.00 |
| 10 | 2016-01-02 | 459 | 610.0 | 607.0 | 582.0 | 581.0 | 582.0 | 580.0 | 582.0 | 582.0 | 584.0 | 584.0 | 583.0 | 593.0 | 593.0 | 592.0 | 587.0 | 587.0 | 604.0 | 604.0 | 604.0 | 604.0 | 571.0 | 571.0 | 568.0 | 558.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 11.0 | 2.0 | 14.0 | 32.0 | 265.0 | 1553 | 586.217391 | 0.25 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1093 | 2016-03-31 | 1264 | 989.0 | 977.0 | 977.0 | 977.0 | 977.0 | 977.0 | 977.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 947.0 | 1.0 | 0.0 | 1.0 | 1.0 | 1.0 | 995.0 | 433.0 | 463.0 | 340.0 | 1062.0 | 2433 | 954.826087 | 0.75 |
| 1094 | 2016-03-31 | 1461 | 882.0 | 884.0 | 884.0 | 884.0 | 884.0 | 884.0 | 884.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 913.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 102.0 | 85.0 | 243.0 | 1104.0 | 4330.0 | 1022 | 905.434783 | 0.50 |
| 1095 | 2016-03-31 | 1490 | 547.0 | 587.0 | 587.0 | 587.0 | 587.0 | 587.0 | 587.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 639.0 | 2.0 | 0.0 | 0.0 | 1.0 | 1.0 | 284.0 | 108.0 | 202.0 | 342.0 | 1030.0 | 1223 | 625.434783 | 0.50 |
| 1096 | 2016-03-31 | 1874 | 545.0 | 571.0 | 571.0 | 571.0 | 571.0 | 571.0 | 571.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 583.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 11.0 | 0.0 | 1.0 | 1.0 | 11.0 | 237 | 579.869565 | 0.00 |
| 1097 | 2016-03-31 | 2346 | 362.0 | 377.0 | 377.0 | 377.0 | 377.0 | 377.0 | 377.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 394.0 | 2.0 | 3.0 | 3.0 | 2.0 | 0.0 | 39.0 | 43.0 | 73.0 | 110.0 | 127.0 | 11099 | 389.565217 | 2.00 |
| 1098 | 2016-03-31 | 2398 | 935.0 | 916.0 | 916.0 | 916.0 | 916.0 | 916.0 | 916.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 919.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.0 | 17.0 | 22.0 | 28.0 | 122.0 | 1372 | 918.217391 | 0.00 |
| 1099 | 2016-03-31 | 2667 | 475.0 | 488.0 | 488.0 | 488.0 | 488.0 | 488.0 | 488.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 476.0 | 1.0 | 1.0 | 0.0 | 4.0 | 2.0 | 155.0 | 105.0 | 157.0 | 225.0 | 347.0 | 3294 | 479.130435 | 1.75 |
| 1100 | 2016-03-31 | 2891 | 20.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 22.0 | 3.0 | 1.0 | 2.0 | 7.0 | 21.0 | 219.0 | 130.0 | 277.0 | 591.0 | 2432.0 | 35903 | 21.739130 | 7.75 |
| 1101 | 2016-03-31 | 3308 | 964.0 | 904.0 | 904.0 | 904.0 | 904.0 | 904.0 | 904.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 959.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 | 1.0 | 9.0 | 8.0 | 49.0 | 777 | 944.652174 | 0.25 |
| 1102 | 2016-03-31 | 3373 | 282.0 | 310.0 | 310.0 | 310.0 | 310.0 | 310.0 | 310.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 314.0 | 0.0 | 1.0 | 0.0 | 5.0 | 7.0 | 5.0 | 3.0 | 6.0 | 31.0 | 100.0 | 3770 | 312.956522 | 3.25 |
919 rows × 39 columns
As we can see in the above dataframe, we have an hourly ranking of applications on a daily basis. An important thing to note is that these ranks are deviating within a given short range of values. In such cases, we can also take the average value of all the ranks and ratings to simplify our problem and then create a single column for both the properties.
subsetDataFrame['avgrank'] = subsetDataFrame.iloc[:,3:26].mean(axis=1)
subsetDataFrame['avgrating'] = subsetDataFrame.iloc[:,27:31].mean(axis=1)newData = subsetDataFrame[['Date','app_id','sales','avgrank','avgrating']]We will now only consider the average rank & rating columns instead of hourly rank columns. For this, we subsetted the data and extracted only the relevant columns in a new data frame.
print(newData) Date app_id sales avgrank avgrating
0 2016-01-01 320 2412 469.739130 0.50
1 2016-01-01 406 1308 719.173913 0.00
2 2016-01-01 459 2037 603.695652 0.25
3 2016-01-01 722 2052 567.913043 2.75
4 2016-01-01 1234 1553 776.826087 0.00
5 2016-01-01 1490 2152 518.260870 0.50
7 2016-01-01 2891 89289 5.782609 33.50
8 2016-01-02 320 1622 464.043478 0.00
9 2016-01-02 406 1110 754.652174 0.00
10 2016-01-02 459 1553 586.217391 0.25
11 2016-01-02 722 1467 617.782609 1.00
12 2016-01-02 1234 1011 727.695652 0.00
13 2016-01-02 1490 1722 546.260870 0.50
16 2016-01-02 2891 84915 5.826087 35.25
17 2016-01-03 320 1564 508.695652 0.00
18 2016-01-03 406 963 762.434783 0.00
19 2016-01-03 459 1409 560.521739 0.00
20 2016-01-03 722 1371 676.000000 0.50
21 2016-01-03 1234 938 829.217391 0.00
22 2016-01-03 1490 1662 560.913043 0.00
23 2016-01-03 2346 2029 680.086957 0.00
24 2016-01-03 2891 61345 5.000000 24.25
25 2016-01-04 320 1609 525.695652 0.00
26 2016-01-04 406 1168 797.217391 0.00
27 2016-01-04 459 1732 573.826087 0.00
28 2016-01-04 722 1421 715.043478 0.50
29 2016-01-04 1234 989 840.652174 0.00
30 2016-01-04 1490 1081 573.782609 1.00
31 2016-01-04 2346 1460 654.565217 0.25
32 2016-01-04 2891 58943 6.391304 24.75
33 2016-01-05 320 1594 548.173913 0.00
34 2016-01-05 406 1163 750.869565 0.00
35 2016-01-05 459 1392 563.130435 0.25
36 2016-01-05 722 1482 757.086957 1.00
37 2016-01-05 1234 1055 875.173913 0.25
38 2016-01-05 1490 984 743.217391 0.00
39 2016-01-05 2346 1102 807.869565 0.50
40 2016-01-05 2891 62196 10.782609 11.25
41 2016-01-06 320 1883 545.739130 0.00
42 2016-01-06 406 1252 742.173913 0.00
44 2016-01-06 722 1737 706.608696 1.00
45 2016-01-06 1234 1227 855.782609 0.00
46 2016-01-06 1490 1244 848.260870 1.25
49 2016-01-06 2891 67759 10.478261 14.50
50 2016-01-07 320 1978 527.478261 0.00
51 2016-01-07 406 1254 763.217391 0.00
53 2016-01-07 722 1538 701.173913 1.25
54 2016-01-07 1234 1125 845.086957 0.00
55 2016-01-07 1490 1184 878.869565 0.25
56 2016-01-07 2398 956 940.260870 0.00
57 2016-01-07 2891 60462 9.434783 13.25
58 2016-01-08 320 1509 483.608696 0.00
59 2016-01-08 406 960 736.043478 0.00
61 2016-01-08 722 1273 702.695652 0.50
62 2016-01-08 1234 893 818.695652 0.00
63 2016-01-08 1490 876 899.391304 0.00
65 2016-01-08 2891 36569 11.608696 10.00
66 2016-01-09 320 1256 502.652174 0.00
67 2016-01-09 406 761 783.478261 0.00
68 2016-01-09 459 867 657.913043 0.25
69 2016-01-09 722 1038 705.956522 2.00
70 2016-01-09 1234 808 789.695652 0.00
71 2016-01-09 1490 871 872.608696 0.50
72 2016-01-09 2891 28872 16.695652 4.25
73 2016-01-10 320 1316 519.652174 0.25
74 2016-01-10 406 775 815.913043 0.00
76 2016-01-10 722 1248 702.260870 0.00
78 2016-01-10 1234 795 804.869565 0.50
79 2016-01-10 1490 866 844.000000 0.00
80 2016-01-10 2891 32229 20.608696 6.50
81 2016-01-11 320 1350 524.173913 0.00
82 2016-01-11 406 744 832.000000 0.00
83 2016-01-11 459 1454 430.869565 0.00
84 2016-01-11 722 1387 651.565217 1.50
85 2016-01-11 907 1008 857.695652 0.00
86 2016-01-11 1234 879 824.782609 0.00
87 2016-01-11 1490 1505 811.608696 0.75
90 2016-01-11 2891 30459 19.086957 5.25
91 2016-01-12 320 1650 525.478261 0.00
92 2016-01-12 406 1071 867.130435 0.00
93 2016-01-12 459 1589 481.826087 0.50
94 2016-01-12 722 1622 588.739130 1.50
95 2016-01-12 907 909 923.565217 0.00
96 2016-01-12 1234 946 821.347826 0.25
97 2016-01-12 1490 2339 675.521739 0.25
98 2016-01-12 2346 2047 774.913043 0.25
100 2016-01-12 2891 30254 21.608696 4.50
122 2016-01-14 320 2046 562.434783 0.00
123 2016-01-14 406 1285 824.391304 0.00
124 2016-01-14 459 1893 480.826087 0.00
129 2016-01-14 1490 2371 636.304348 0.25
134 2016-01-14 2891 49142 19.782609 8.25
135 2016-01-15 320 1813 531.565217 0.25
136 2016-01-15 406 1132 843.869565 0.00
137 2016-01-15 459 1478 556.695652 0.00
138 2016-01-15 722 1613 686.086957 0.50
139 2016-01-15 1234 1110 810.347826 0.00
140 2016-01-15 1490 2081 637.739130 0.00
141 2016-01-15 2346 1769 728.521739 0.00
142 2016-01-15 2891 37157 19.173913 10.75
143 2016-01-16 320 1372 493.043478 0.00
144 2016-01-16 406 844 834.304348 0.00
146 2016-01-16 722 1391 685.695652 1.25
147 2016-01-16 1234 900 826.260870 0.00
148 2016-01-16 1490 1554 639.260870 0.25
149 2016-01-16 2346 1443 757.043478 0.00
150 2016-01-16 2891 30890 23.956522 4.25
151 2016-01-17 320 1385 519.478261 0.00
152 2016-01-17 406 892 823.521739 0.00
153 2016-01-17 459 977 624.521739 0.50
154 2016-01-17 722 2729 606.086957 1.25
155 2016-01-17 1234 919 828.739130 0.25
156 2016-01-17 1490 1673 661.000000 1.00
157 2016-01-17 2346 1115 787.478261 1.00
159 2016-01-17 2891 27467 25.695652 5.50
160 2016-01-18 320 1291 546.608696 0.25
161 2016-01-18 406 777 841.695652 0.00
163 2016-01-18 722 3084 433.347826 0.75
164 2016-01-18 1234 804 809.391304 0.00
165 2016-01-18 1490 943 678.217391 0.50
167 2016-01-18 2667 44326 68.956522 10.50
168 2016-01-18 2891 22792 28.304348 3.75
169 2016-01-19 320 1368 545.565217 0.00
170 2016-01-19 406 945 879.913043 0.00
171 2016-01-19 459 1977 733.652174 0.50
172 2016-01-19 722 2754 383.173913 1.00
173 2016-01-19 1234 920 835.869565 0.00
174 2016-01-19 1490 1094 788.434783 0.25
176 2016-01-19 2667 47512 25.739130 6.75
177 2016-01-19 2891 29160 32.608696 5.75
178 2016-01-20 320 1746 565.173913 0.00
179 2016-01-20 406 1267 876.347826 0.00
181 2016-01-20 722 3107 390.652174 0.75
182 2016-01-20 1234 1096 859.695652 0.00
183 2016-01-20 1490 1319 749.478261 0.50
184 2016-01-20 2346 1355 766.130435 0.50
186 2016-01-20 2667 49850 24.826087 12.50
187 2016-01-20 2891 36121 33.000000 8.25
188 2016-01-21 320 1828 555.695652 0.00
189 2016-01-21 406 1124 805.956522 0.00
190 2016-01-21 459 1923 401.782609 0.25
191 2016-01-21 722 2897 407.304348 1.25
192 2016-01-21 1234 1220 848.739130 0.25
193 2016-01-21 1490 1399 714.043478 0.00
194 2016-01-21 2346 1582 854.217391 0.00
195 2016-01-21 2490 1958 838.695652 0.25
196 2016-01-21 2667 45371 27.652174 8.25
197 2016-01-21 2891 34248 35.478261 7.50
198 2016-01-22 320 1232 514.913043 0.25
199 2016-01-22 406 727 821.478261 0.00
200 2016-01-22 459 1532 462.260870 0.25
201 2016-01-22 722 2090 431.043478 0.75
202 2016-01-22 1234 842 768.130435 0.00
203 2016-01-22 1490 1043 692.086957 0.50
204 2016-01-22 2346 1284 892.043478 0.25
205 2016-01-22 2490 1654 667.043478 0.50
206 2016-01-22 2667 28863 29.000000 6.75
207 2016-01-22 2891 21218 35.913043 6.00
208 2016-01-23 320 1167 502.173913 0.00
209 2016-01-23 406 798 847.739130 0.00
210 2016-01-23 459 1198 479.521739 0.25
211 2016-01-23 722 2135 433.043478 1.00
212 2016-01-23 1234 775 769.173913 0.25
213 2016-01-23 1490 856 679.826087 0.00
214 2016-01-23 2346 1276 794.826087 0.25
215 2016-01-23 2490 1192 629.391304 0.25
218 2016-01-24 320 1189 532.695652 0.00
219 2016-01-24 406 708 862.695652 0.00
220 2016-01-24 459 2028 474.652174 0.00
221 2016-01-24 722 1781 450.130435 3.75
223 2016-01-24 1234 794 802.521739 0.00
224 2016-01-24 1490 886 737.347826 0.25
225 2016-01-24 2346 1120 750.782609 0.75
226 2016-01-24 2490 1168 645.434783 0.00
227 2016-01-24 2667 21527 36.782609 6.75
228 2016-01-24 2891 18949 32.826087 4.50
229 2016-01-25 320 1250 539.304348 0.00
230 2016-01-25 406 779 894.130435 0.00
231 2016-01-25 459 1336 423.304348 0.25
232 2016-01-25 722 1703 501.217391 4.00
233 2016-01-25 907 1019 928.956522 0.00
234 2016-01-25 1234 791 870.000000 0.25
235 2016-01-25 1490 935 732.086957 0.00
236 2016-01-25 2346 1087 811.565217 0.25
237 2016-01-25 2490 1259 726.565217 0.50
238 2016-01-25 2667 16278 46.739130 4.25
239 2016-01-25 2891 20721 36.869565 2.75
240 2016-01-26 320 1368 545.782609 0.00
241 2016-01-26 406 924 902.130435 0.00
242 2016-01-26 459 1431 520.347826 0.00
243 2016-01-26 722 2316 507.782609 1.75
244 2016-01-26 907 949 932.826087 0.50
245 2016-01-26 1234 931 843.956522 0.25
246 2016-01-26 1490 1029 723.521739 0.00
248 2016-01-26 2490 1204 702.347826 0.00
249 2016-01-26 2667 16522 63.478261 4.00
250 2016-01-26 2891 27454 33.217391 3.50
251 2016-01-27 320 1807 553.434783 0.00
252 2016-01-27 406 1317 874.086957 0.00
253 2016-01-27 459 2079 520.347826 0.50
254 2016-01-27 722 2487 482.565217 3.25
256 2016-01-27 1234 1157 853.130435 0.00
257 2016-01-27 1490 1213 757.086957 0.25
259 2016-01-27 2490 1758 746.652174 0.50
260 2016-01-27 2667 18981 77.739130 3.75
261 2016-01-27 2891 34159 31.391304 6.75
262 2016-01-28 320 1639 554.608696 0.25
263 2016-01-28 406 1188 799.391304 0.00
264 2016-01-28 459 2361 514.521739 0.25
265 2016-01-28 722 2116 505.304348 1.00
266 2016-01-28 1234 1147 840.000000 0.00
267 2016-01-28 1490 1321 781.956522 0.75
269 2016-01-28 2490 1935 726.347826 0.00
270 2016-01-28 2667 17933 85.260870 3.00
271 2016-01-28 2891 34537 30.826087 6.50
272 2016-01-29 320 1240 537.173913 0.00
273 2016-01-29 406 703 816.304348 0.00
274 2016-01-29 459 1480 456.391304 0.25
275 2016-01-29 722 1502 540.739130 2.50
276 2016-01-29 1234 827 842.521739 0.25
277 2016-01-29 1490 863 748.565217 0.75
278 2016-01-29 2398 7055 164.739130 0.00
279 2016-01-29 2490 386 736.652174 0.25
280 2016-01-29 2667 13235 88.956522 3.25
281 2016-01-29 2891 20230 30.913043 3.00
282 2016-01-30 320 1175 529.652174 0.00
283 2016-01-30 406 667 868.956522 0.00
284 2016-01-30 459 1409 467.521739 0.00
285 2016-01-30 722 1727 564.826087 2.75
286 2016-01-30 1234 797 824.173913 0.00
287 2016-01-30 1490 1197 789.565217 0.75
289 2016-01-30 2398 7829 138.565217 0.25
291 2016-01-30 2667 13021 87.304348 1.75
292 2016-01-30 2891 16766 38.478261 4.75
294 2016-01-31 320 1149 532.739130 0.25
295 2016-01-31 406 711 896.478261 0.00
296 2016-01-31 459 1207 495.521739 0.25
297 2016-01-31 722 2059 475.086957 6.75
298 2016-01-31 1234 790 781.086957 0.00
299 2016-01-31 1490 739 676.043478 0.50
301 2016-01-31 2398 6435 128.565217 0.00
302 2016-01-31 2667 11656 91.043478 4.75
303 2016-01-31 2891 15648 45.173913 4.25
304 2016-02-01 320 1270 547.086957 0.25
305 2016-02-01 406 773 884.434783 0.00
306 2016-02-01 459 1271 525.869565 0.00
307 2016-02-01 722 1649 473.521739 1.50
309 2016-02-01 1234 928 777.652174 0.00
310 2016-02-01 1490 751 772.565217 0.00
312 2016-02-01 2398 6630 134.260870 0.00
313 2016-02-01 2667 8485 105.260870 1.75
314 2016-02-01 2891 16267 50.565217 4.50
316 2016-02-02 320 1435 549.434783 0.00
317 2016-02-02 406 1003 873.434783 0.00
318 2016-02-02 459 1181 588.391304 0.25
319 2016-02-02 722 1770 505.000000 0.50
320 2016-02-02 1234 1080 747.652174 0.25
323 2016-02-02 2398 4731 155.739130 0.25
324 2016-02-02 2667 8677 145.565217 1.50
325 2016-02-02 2891 18581 56.521739 3.75
327 2016-02-03 320 1858 563.478261 0.00
328 2016-02-03 406 1419 817.521739 0.00
329 2016-02-03 459 1409 630.000000 0.25
330 2016-02-03 722 2330 540.521739 2.50
331 2016-02-03 1234 1361 768.043478 0.00
333 2016-02-03 2346 2569 765.043478 0.00
334 2016-02-03 2398 6081 197.130435 0.50
335 2016-02-03 2667 9948 163.565217 1.25
336 2016-02-03 2891 25123 57.173913 5.00
339 2016-02-04 320 1869 537.782609 0.00
340 2016-02-04 406 1312 813.956522 0.00
341 2016-02-04 459 1678 648.086957 0.00
342 2016-02-04 722 2164 550.304348 1.50
343 2016-02-04 1234 1254 737.217391 0.00
345 2016-02-04 2346 3304 621.826087 0.00
346 2016-02-04 2398 4468 228.086957 0.00
347 2016-02-04 2667 9212 183.956522 3.00
348 2016-02-04 2891 23394 59.565217 7.75
349 2016-02-04 3082 6235 262.086957 1.25
351 2016-02-05 320 1320 520.086957 0.25
352 2016-02-05 406 846 800.695652 0.00
353 2016-02-05 459 1293 616.869565 0.00
354 2016-02-05 722 1449 561.782609 0.50
355 2016-02-05 1234 925 754.173913 0.25
356 2016-02-05 2346 2616 541.304348 0.00
357 2016-02-05 2398 2302 293.695652 0.00
358 2016-02-05 2667 6666 186.130435 2.00
359 2016-02-05 2891 14004 60.043478 3.50
360 2016-02-05 3082 3760 248.391304 0.75
362 2016-02-06 320 1280 533.695652 0.00
363 2016-02-06 406 769 808.304348 0.00
364 2016-02-06 459 1014 612.695652 0.25
365 2016-02-06 722 1769 557.695652 1.25
366 2016-02-06 1234 925 759.521739 0.00
367 2016-02-06 2346 2343 530.391304 0.00
368 2016-02-06 2398 1973 386.956522 0.00
369 2016-02-06 2667 6154 177.565217 9.25
370 2016-02-06 2891 13272 65.478261 3.00
371 2016-02-06 3082 3408 295.260870 1.00
372 2016-02-06 3333 3552 364.260870 0.75
373 2016-02-07 320 1439 536.434783 0.00
374 2016-02-07 406 929 850.434783 0.00
375 2016-02-07 459 979 654.217391 0.50
376 2016-02-07 722 1663 515.086957 1.25
377 2016-02-07 1234 989 789.565217 0.25
378 2016-02-07 2346 2189 521.739130 0.50
379 2016-02-07 2398 1938 458.608696 0.00
380 2016-02-07 2667 6039 181.217391 7.50
381 2016-02-07 2891 12973 69.869565 2.00
382 2016-02-07 3082 3955 334.391304 1.75
383 2016-02-07 3333 3062 379.000000 0.00
384 2016-02-08 320 1359 530.869565 0.00
385 2016-02-08 406 815 838.304348 0.00
386 2016-02-08 459 1439 692.521739 0.25
387 2016-02-08 722 1667 554.956522 1.25
388 2016-02-08 1234 927 768.913043 0.00
389 2016-02-08 2346 2127 516.391304 0.00
390 2016-02-08 2398 1618 515.260870 0.00
391 2016-02-08 2667 5211 193.304348 2.50
392 2016-02-08 2891 11992 78.173913 2.25
393 2016-02-08 3082 1317 335.000000 0.75
394 2016-02-08 3333 2002 435.304348 0.00
395 2016-02-09 320 1735 521.478261 0.00
396 2016-02-09 406 1020 816.739130 0.00
397 2016-02-09 459 1465 580.739130 0.00
398 2016-02-09 722 2006 607.391304 1.00
400 2016-02-09 1234 1088 752.304348 0.00
401 2016-02-09 2346 2027 548.434783 0.00
402 2016-02-09 2398 1568 558.826087 0.00
403 2016-02-09 2667 5879 210.043478 3.00
404 2016-02-09 2891 15179 78.130435 3.00
405 2016-02-09 3082 556 606.913043 0.75
406 2016-02-09 3333 2652 537.695652 0.00
407 2016-02-10 320 2212 527.739130 0.25
408 2016-02-10 406 1385 764.434783 0.00
409 2016-02-10 459 1697 581.565217 0.50
410 2016-02-10 722 2673 575.869565 1.75
411 2016-02-10 1234 1448 762.478261 0.00
412 2016-02-10 2346 3024 627.956522 0.25
413 2016-02-10 2398 1736 650.608696 0.00
414 2016-02-10 2667 6547 229.260870 3.75
415 2016-02-10 2891 20504 80.869565 4.50
418 2016-02-10 3333 2061 569.391304 0.25
419 2016-02-11 320 2064 481.347826 0.00
420 2016-02-11 406 1238 785.565217 0.00
421 2016-02-11 459 1760 622.608696 0.00
422 2016-02-11 722 2236 552.000000 1.25
423 2016-02-11 1234 1381 714.173913 0.00
424 2016-02-11 2346 3513 602.695652 0.00
425 2016-02-11 2398 1719 707.347826 0.00
426 2016-02-11 2667 6110 261.956522 4.50
427 2016-02-11 2891 17485 76.695652 5.00
429 2016-02-11 3333 1916 794.652174 0.25
430 2016-02-12 320 1449 467.000000 0.00
431 2016-02-12 406 791 825.173913 0.00
432 2016-02-12 459 1235 604.130435 0.50
433 2016-02-12 722 1489 593.043478 0.50
434 2016-02-12 1234 1001 709.000000 0.00
435 2016-02-12 2346 2534 523.347826 1.00
436 2016-02-12 2398 1232 723.000000 0.00
437 2016-02-12 2667 4729 272.521739 1.50
438 2016-02-12 2891 11147 79.086957 4.25
442 2016-02-13 320 1342 487.478261 0.00
443 2016-02-13 406 702 858.956522 0.00
444 2016-02-13 459 1728 563.434783 0.50
445 2016-02-13 722 1380 606.695652 1.00
447 2016-02-13 1234 1009 678.956522 0.00
448 2016-02-13 2346 2332 462.000000 0.75
450 2016-02-13 2398 1271 739.304348 0.25
451 2016-02-13 2667 4395 261.391304 2.50
452 2016-02-13 2891 9598 82.260870 2.50
454 2016-02-14 82 2928 223.304348 0.00
455 2016-02-14 320 1280 506.347826 0.00
456 2016-02-14 406 674 904.260870 0.00
457 2016-02-14 459 949 473.565217 0.25
458 2016-02-14 722 1353 625.956522 0.25
459 2016-02-14 907 746 924.086957 0.25
460 2016-02-14 1234 877 684.695652 0.25
461 2016-02-14 2346 1954 451.130435 0.25
462 2016-02-14 2373 826 742.739130 1.00
463 2016-02-14 2398 1090 701.347826 0.00
464 2016-02-14 2667 4079 261.043478 3.00
465 2016-02-14 2891 10271 87.956522 3.50
468 2016-02-15 82 2029 278.826087 0.25
469 2016-02-15 320 1430 485.391304 0.00
470 2016-02-15 406 768 902.086957 0.00
471 2016-02-15 459 940 562.173913 0.00
472 2016-02-15 722 1441 606.608696 1.25
474 2016-02-15 1234 969 692.000000 0.00
476 2016-02-15 2346 2134 479.173913 0.25
478 2016-02-15 2398 1316 726.478261 0.00
479 2016-02-15 2667 4924 253.956522 2.25
480 2016-02-15 2891 11795 78.000000 3.75
483 2016-02-16 82 1851 415.913043 0.25
484 2016-02-16 320 1582 480.956522 0.00
485 2016-02-16 406 1000 888.434783 0.00
486 2016-02-16 459 1409 644.608696 0.00
487 2016-02-16 722 1997 598.000000 0.50
488 2016-02-16 1234 1079 726.913043 0.25
489 2016-02-16 1490 1562 867.652174 6.75
490 2016-02-16 2346 2062 503.173913 0.00
491 2016-02-16 2398 1478 710.826087 0.00
492 2016-02-16 2667 5353 251.782609 2.75
493 2016-02-16 2891 15537 75.478261 5.00
494 2016-02-17 82 1723 561.956522 0.25
495 2016-02-17 320 1919 503.304348 0.00
496 2016-02-17 406 1307 865.000000 0.00
497 2016-02-17 459 1661 582.913043 0.25
498 2016-02-17 722 2217 562.391304 0.75
499 2016-02-17 1234 1327 750.565217 0.00
500 2016-02-17 1490 1960 807.521739 2.25
501 2016-02-17 2346 2706 569.739130 0.00
502 2016-02-17 2398 1865 747.043478 0.25
503 2016-02-17 2667 8867 265.608696 1.50
504 2016-02-17 2891 20417 74.652174 5.75
506 2016-02-18 82 1419 705.086957 0.00
507 2016-02-18 320 2025 506.782609 0.00
508 2016-02-18 406 1293 802.217391 0.00
509 2016-02-18 459 1821 584.217391 0.25
510 2016-02-18 722 2385 574.869565 0.75
511 2016-02-18 1234 1310 771.304348 0.00
512 2016-02-18 1490 1734 835.739130 1.50
513 2016-02-18 2346 3556 552.391304 0.00
514 2016-02-18 2398 2286 733.695652 0.25
515 2016-02-18 2667 35279 184.565217 5.00
516 2016-02-18 2891 20284 72.043478 7.00
518 2016-02-19 82 1357 798.869565 0.00
519 2016-02-19 320 1560 505.173913 0.00
520 2016-02-19 406 1021 810.000000 0.00
521 2016-02-19 459 715 628.000000 0.25
522 2016-02-19 722 1969 533.086957 1.75
523 2016-02-19 1234 1170 775.478261 0.25
524 2016-02-19 1490 1476 859.521739 3.00
525 2016-02-19 2346 3099 496.043478 0.50
526 2016-02-19 2398 1933 668.304348 0.00
527 2016-02-19 2667 16330 78.826087 2.75
528 2016-02-19 2891 19032 71.130435 4.75
531 2016-02-20 320 1302 513.260870 0.00
532 2016-02-20 406 856 844.695652 0.00
533 2016-02-20 459 1271 794.260870 0.00
534 2016-02-20 722 1555 542.043478 1.25
535 2016-02-20 1234 946 780.913043 0.25
536 2016-02-20 1490 1163 839.000000 1.00
537 2016-02-20 2346 3295 443.869565 0.25
538 2016-02-20 2398 1310 670.000000 0.00
539 2016-02-20 2667 10265 98.652174 2.00
540 2016-02-20 2891 14375 64.173913 6.00
542 2016-02-21 320 1357 508.869565 0.25
543 2016-02-21 406 799 826.521739 0.00
544 2016-02-21 459 1187 679.913043 0.00
545 2016-02-21 722 1413 560.130435 0.50
546 2016-02-21 1234 972 771.695652 0.00
547 2016-02-21 1490 1138 880.826087 0.25
548 2016-02-21 2346 3777 381.652174 0.50
550 2016-02-21 2398 1256 706.695652 0.00
551 2016-02-21 2667 8114 119.260870 2.25
552 2016-02-21 2891 14902 63.478261 3.00
554 2016-02-22 320 1396 508.086957 0.00
555 2016-02-22 406 789 821.217391 0.00
556 2016-02-22 459 1086 623.347826 0.25
557 2016-02-22 722 1486 600.391304 1.00
558 2016-02-22 1234 1024 735.217391 0.25
559 2016-02-22 1490 806 882.956522 0.00
560 2016-02-22 2346 5447 321.782609 0.00
561 2016-02-22 2373 820 657.739130 1.00
562 2016-02-22 2398 1297 741.391304 0.00
563 2016-02-22 2667 7352 150.478261 2.75
564 2016-02-22 2891 18591 60.608696 2.75
567 2016-02-23 320 1654 499.739130 0.00
568 2016-02-23 406 1059 848.130435 0.25
569 2016-02-23 459 1381 589.217391 0.00
570 2016-02-23 722 1763 610.130435 0.25
572 2016-02-23 1234 1149 717.173913 0.00
574 2016-02-23 2346 3896 349.652174 0.00
576 2016-02-23 2398 1469 736.043478 0.00
577 2016-02-23 2667 7706 163.869565 1.75
578 2016-02-23 2891 21589 45.000000 4.00
581 2016-02-24 320 2128 492.913043 0.00
582 2016-02-24 406 1373 777.217391 0.00
583 2016-02-24 459 1614 581.608696 1.00
585 2016-02-24 722 2165 589.608696 2.00
586 2016-02-24 1234 1373 706.869565 0.00
588 2016-02-24 2346 3034 431.347826 0.50
589 2016-02-24 2398 2178 718.782609 0.25
590 2016-02-24 2667 8076 187.347826 3.00
592 2016-02-24 2891 29501 40.173913 5.50
593 2016-02-25 320 1936 477.739130 0.00
594 2016-02-25 406 1227 760.391304 0.00
595 2016-02-25 459 1717 638.000000 0.50
596 2016-02-25 676 983 933.434783 0.00
597 2016-02-25 722 1981 595.086957 1.50
598 2016-02-25 1234 1319 734.652174 0.00
600 2016-02-25 2346 3048 529.739130 0.00
601 2016-02-25 2398 1689 693.478261 0.00
602 2016-02-25 2667 5546 221.956522 1.75
603 2016-02-25 2821 1647 619.304348 0.25
604 2016-02-25 2891 31090 35.304348 10.00
605 2016-02-26 320 1480 468.173913 0.00
606 2016-02-26 406 811 774.130435 0.00
607 2016-02-26 459 1055 620.739130 0.25
608 2016-02-26 676 627 925.173913 0.00
609 2016-02-26 722 1183 612.086957 0.50
610 2016-02-26 1234 962 725.130435 0.00
612 2016-02-26 2346 2712 509.913043 0.00
613 2016-02-26 2398 1020 762.695652 0.00
614 2016-02-26 2667 4138 266.304348 2.25
615 2016-02-26 2821 176 648.521739 0.25
616 2016-02-26 2891 16154 34.521739 5.00
618 2016-02-27 320 1458 422.956522 0.00
619 2016-02-27 406 700 785.695652 0.25
620 2016-02-27 459 865 646.869565 0.00
621 2016-02-27 676 563 934.956522 0.00
622 2016-02-27 722 1314 668.826087 3.00
624 2016-02-27 1234 892 696.913043 0.25
625 2016-02-27 2346 2770 426.565217 0.75
626 2016-02-27 2398 1199 781.130435 0.00
627 2016-02-27 2667 4485 271.695652 1.00
629 2016-02-27 2891 15148 41.130435 5.25
632 2016-02-28 320 1430 414.217391 0.25
633 2016-02-28 406 768 803.000000 0.00
634 2016-02-28 459 1364 670.347826 0.00
636 2016-02-28 722 1411 618.869565 5.75
638 2016-02-28 1234 951 708.826087 0.00
639 2016-02-28 2346 2600 374.608696 0.50
640 2016-02-28 2398 1438 758.260870 0.25
641 2016-02-28 2667 4460 260.695652 1.75
642 2016-02-28 2891 15738 43.478261 5.75
643 2016-02-28 3308 1047 880.260870 0.25
645 2016-03-01 320 1586 423.913043 0.25
646 2016-03-01 406 853 796.217391 0.00
647 2016-03-01 459 1590 560.869565 0.50
648 2016-03-01 676 662 957.086957 0.00
649 2016-03-01 722 3075 571.608696 2.50
651 2016-03-01 1234 979 683.739130 0.00
653 2016-03-01 2346 1548 398.913043 0.50
654 2016-03-01 2398 1401 706.304348 0.00
655 2016-03-01 2667 5233 245.565217 2.75
656 2016-03-01 2891 19151 39.782609 2.00
657 2016-03-01 3308 1200 745.434783 0.50
659 2016-03-02 320 2041 417.260870 0.00
660 2016-03-02 406 1012 813.565217 0.00
661 2016-03-02 459 1786 506.043478 0.25
662 2016-03-02 676 823 928.217391 0.00
663 2016-03-02 722 4436 431.434783 0.75
664 2016-03-02 1234 1114 681.217391 0.00
667 2016-03-02 2333 4691 477.260870 0.25
668 2016-03-02 2346 1542 537.043478 0.00
669 2016-03-02 2398 2221 635.086957 0.00
670 2016-03-02 2667 5296 223.304348 1.50
671 2016-03-02 2891 25378 34.826087 4.50
672 2016-03-02 3308 1252 663.565217 0.00
673 2016-03-03 320 2322 393.304348 0.25
674 2016-03-03 406 1277 805.869565 0.00
675 2016-03-03 459 1508 524.391304 0.25
676 2016-03-03 676 1049 924.826087 0.00
677 2016-03-03 722 5262 371.695652 1.50
678 2016-03-03 1234 1294 716.826087 0.00
679 2016-03-03 1490 1317 872.565217 0.75
680 2016-03-03 2333 6659 327.434783 0.00
681 2016-03-03 2346 1706 683.217391 0.25
682 2016-03-03 2398 2198 505.304348 0.00
683 2016-03-03 2667 5575 253.652174 2.25
684 2016-03-03 2891 33408 29.565217 4.00
685 2016-03-03 3308 1982 662.434783 0.25
687 2016-03-04 320 2084 404.826087 0.00
688 2016-03-04 406 1123 801.304348 0.00
689 2016-03-04 459 1720 594.521739 0.50
690 2016-03-04 676 990 915.913043 0.00
691 2016-03-04 722 4766 364.130435 1.50
692 2016-03-04 1234 1396 752.217391 0.00
693 2016-03-04 1490 1115 904.434783 0.75
694 2016-03-04 2333 6417 253.739130 0.25
695 2016-03-04 2346 1925 776.739130 0.00
696 2016-03-04 2398 1691 573.000000 0.25
698 2016-03-04 2667 5401 291.782609 4.25
699 2016-03-04 2891 32011 30.565217 6.25
700 2016-03-04 3308 2568 612.521739 0.50
701 2016-03-05 320 1581 405.043478 0.00
702 2016-03-05 406 664 823.173913 0.25
703 2016-03-05 459 1888 566.000000 0.00
704 2016-03-05 676 631 892.000000 0.00
705 2016-03-05 722 3108 368.608696 1.75
707 2016-03-05 1234 3348 572.695652 0.00
708 2016-03-05 1490 1131 890.869565 0.00
709 2016-03-05 2333 4743 255.695652 0.50
710 2016-03-05 2346 1862 716.565217 0.00
711 2016-03-05 2398 937 695.913043 0.00
713 2016-03-05 2667 3862 293.869565 0.75
714 2016-03-05 2891 32532 28.869565 2.25
715 2016-03-05 3308 2426 496.521739 0.25
717 2016-03-06 320 1527 410.434783 0.00
718 2016-03-06 406 757 886.260870 0.00
719 2016-03-06 459 1526 437.260870 0.25
720 2016-03-06 676 786 832.913043 0.00
721 2016-03-06 722 3066 382.000000 1.25
722 2016-03-06 907 928 922.652174 0.00
723 2016-03-06 1234 1710 350.260870 0.00
724 2016-03-06 1490 1391 779.956522 1.25
726 2016-03-06 2333 2689 246.260870 1.00
727 2016-03-06 2346 1827 603.695652 0.25
728 2016-03-06 2398 990 778.913043 0.00
729 2016-03-06 2667 4908 289.695652 1.00
730 2016-03-06 2891 33458 17.130435 4.00
731 2016-03-06 3308 1779 412.652174 0.25
733 2016-03-07 320 1603 431.521739 0.00
734 2016-03-07 406 798 928.130435 0.00
735 2016-03-07 459 1207 496.043478 0.00
736 2016-03-07 676 1008 780.260870 0.00
737 2016-03-07 722 3151 398.304348 0.75
739 2016-03-07 1234 1368 425.695652 0.00
740 2016-03-07 1490 1529 659.130435 1.50
741 2016-03-07 2333 1731 354.260870 1.00
742 2016-03-07 2346 2077 595.000000 0.25
743 2016-03-07 2398 1188 812.000000 0.00
744 2016-03-07 2667 4436 257.869565 1.00
745 2016-03-07 2891 34919 14.521739 4.50
746 2016-03-07 3308 1640 447.478261 0.50
747 2016-03-08 320 1469 422.608696 0.00
748 2016-03-08 406 711 898.434783 0.00
749 2016-03-08 459 1096 612.956522 0.00
750 2016-03-08 676 757 702.347826 0.00
751 2016-03-08 722 2009 415.086957 0.25
752 2016-03-08 1234 1082 553.043478 0.00
753 2016-03-08 1490 1259 593.217391 0.50
754 2016-03-08 2333 2015 562.565217 0.00
755 2016-03-08 2346 2057 536.130435 0.00
756 2016-03-08 2398 1143 768.130435 0.00
757 2016-03-08 2667 3709 267.260870 0.50
758 2016-03-08 2891 33293 13.652174 5.50
759 2016-03-08 3308 1551 504.565217 0.50
760 2016-03-09 320 1639 436.086957 0.25
761 2016-03-09 406 957 930.086957 0.00
762 2016-03-09 459 1567 623.260870 0.50
763 2016-03-09 676 859 779.782609 0.00
764 2016-03-09 722 1851 507.869565 0.50
765 2016-03-09 1234 1259 628.869565 0.25
767 2016-03-09 1490 1520 613.260870 0.75
769 2016-03-09 2333 2084 595.000000 0.50
770 2016-03-09 2346 1915 497.652174 0.00
771 2016-03-09 2398 1290 829.478261 0.00
772 2016-03-09 2667 3816 303.826087 0.75
773 2016-03-09 2891 35441 13.565217 6.25
774 2016-03-09 3308 2183 473.173913 0.75
775 2016-03-10 320 1919 475.391304 0.00
776 2016-03-10 406 1280 902.000000 0.00
777 2016-03-10 459 1869 532.521739 0.25
778 2016-03-10 676 1209 801.130435 0.00
779 2016-03-10 722 2006 518.043478 1.00
780 2016-03-10 1234 1477 645.000000 0.00
781 2016-03-10 1461 998 877.217391 0.50
782 2016-03-10 1490 1766 607.434783 0.50
783 2016-03-10 1874 19010 135.608696 0.00
784 2016-03-10 2333 2478 618.000000 0.25
785 2016-03-10 2346 2616 571.478261 0.50
786 2016-03-10 2398 1957 774.956522 0.00
787 2016-03-10 2667 4539 333.391304 1.75
788 2016-03-10 2891 41386 15.826087 6.00
789 2016-03-10 3308 2946 408.347826 0.00
805 2016-03-12 320 1395 481.565217 0.00
806 2016-03-12 406 895 906.260870 0.00
807 2016-03-12 459 1154 575.956522 0.25
808 2016-03-12 676 919 752.565217 0.00
809 2016-03-12 722 1231 650.043478 0.75
810 2016-03-12 1234 874 730.173913 0.00
812 2016-03-12 1490 1602 657.086957 1.25
813 2016-03-12 1874 16656 45.130435 0.00
814 2016-03-12 2333 1771 557.260870 0.50
815 2016-03-12 2346 2661 457.869565 0.00
816 2016-03-12 2398 993 873.608696 0.00
817 2016-03-12 2667 3257 365.086957 0.75
818 2016-03-12 2891 28069 23.000000 2.50
819 2016-03-12 3308 1447 404.913043 0.00
820 2016-03-13 320 1772 477.130435 0.00
821 2016-03-13 406 833 891.086957 0.00
822 2016-03-13 459 1127 615.434783 0.25
823 2016-03-13 676 1036 747.739130 0.00
824 2016-03-13 722 1188 691.826087 0.00
825 2016-03-13 1234 898 764.565217 0.00
826 2016-03-13 1461 2082 442.956522 0.75
827 2016-03-13 1490 1502 599.130435 1.25
828 2016-03-13 1874 4107 52.217391 0.25
829 2016-03-13 2333 1385 595.260870 0.00
830 2016-03-13 2346 2750 439.130435 0.00
831 2016-03-13 2398 1027 931.608696 0.00
832 2016-03-13 2667 2941 371.608696 1.25
833 2016-03-13 2891 28991 22.826087 3.25
834 2016-03-13 3308 872 531.130435 0.00
835 2016-03-14 320 1528 431.565217 0.00
836 2016-03-14 406 738 921.869565 0.00
837 2016-03-14 459 1084 654.652174 0.00
838 2016-03-14 676 871 783.043478 0.00
839 2016-03-14 722 1163 734.521739 0.75
840 2016-03-14 1234 880 771.608696 0.00
841 2016-03-14 1461 1433 507.608696 1.50
842 2016-03-14 1490 1039 607.478261 1.75
844 2016-03-14 1874 1761 116.565217 0.00
845 2016-03-14 2333 1777 683.217391 0.50
846 2016-03-14 2346 2917 411.739130 0.50
847 2016-03-14 2398 909 891.173913 0.00
848 2016-03-14 2667 3078 405.478261 0.50
849 2016-03-14 2891 27801 20.782609 4.50
850 2016-03-14 3308 815 710.913043 0.00
851 2016-03-15 320 1614 416.173913 0.25
852 2016-03-15 406 758 931.260870 0.00
853 2016-03-15 459 873 689.608696 0.00
854 2016-03-15 676 743 778.521739 0.00
855 2016-03-15 722 1139 751.695652 0.75
856 2016-03-15 1234 953 764.782609 0.00
857 2016-03-15 1461 1653 639.478261 0.00
858 2016-03-15 1490 787 708.260870 0.25
859 2016-03-15 1498 443 622.652174 0.00
860 2016-03-15 1874 472 268.913043 0.00
861 2016-03-15 2333 1107 634.000000 0.00
862 2016-03-15 2346 2533 398.782609 0.00
863 2016-03-15 2398 1234 912.608696 0.00
864 2016-03-15 2667 3078 385.000000 1.50
865 2016-03-15 2891 27124 18.869565 2.50
866 2016-03-15 3308 674 854.347826 0.50
868 2016-03-16 320 1639 432.000000 0.00
869 2016-03-16 406 1088 981.000000 0.00
870 2016-03-16 459 1643 816.000000 0.00
871 2016-03-16 676 1036 822.000000 0.00
872 2016-03-16 722 1288 778.000000 2.00
873 2016-03-16 1234 929 757.000000 0.00
874 2016-03-16 1461 1993 632.000000 0.50
875 2016-03-16 1490 779 824.000000 0.00
876 2016-03-16 1498 151 826.000000 0.25
877 2016-03-16 1874 48 463.000000 0.00
878 2016-03-16 2333 913 725.000000 0.00
879 2016-03-16 2346 2490 429.000000 0.25
880 2016-03-16 2398 2112 807.000000 0.25
881 2016-03-16 2667 3049 402.000000 1.00
882 2016-03-16 2891 30309 20.000000 2.50
883 2016-03-16 3308 704 957.000000 0.00
884 2016-03-16 3373 23134 121.000000 2.00
885 2016-03-17 320 1826 471.521739 0.00
886 2016-03-17 406 1266 882.695652 0.00
887 2016-03-17 459 1674 632.869565 0.00
888 2016-03-17 676 1286 758.347826 0.00
889 2016-03-17 722 1518 778.347826 1.75
890 2016-03-17 1234 1170 786.434783 0.25
891 2016-03-17 1461 2531 593.826087 1.00
892 2016-03-17 1490 1025 929.652174 2.00
895 2016-03-17 2346 2808 477.695652 0.00
896 2016-03-17 2398 1842 602.391304 0.00
897 2016-03-17 2667 3495 426.434783 1.25
898 2016-03-17 2891 34961 20.478261 7.75
899 2016-03-17 3373 26072 70.565217 3.75
900 2016-03-18 320 1680 499.434783 0.00
901 2016-03-18 406 1114 894.434783 0.00
902 2016-03-18 459 1160 569.391304 0.00
903 2016-03-18 676 1154 719.695652 0.00
904 2016-03-18 722 1367 788.956522 0.75
905 2016-03-18 1234 1143 811.913043 0.00
906 2016-03-18 1461 2260 539.130435 2.25
908 2016-03-18 2346 2897 522.086957 0.50
909 2016-03-18 2398 1793 636.826087 0.00
910 2016-03-18 2667 3170 468.000000 1.25
911 2016-03-18 2891 32913 23.652174 6.25
912 2016-03-18 3373 20929 49.913043 4.50
913 2016-03-19 320 1325 513.260870 0.00
915 2016-03-19 459 698 705.956522 0.00
916 2016-03-19 676 661 746.869565 0.00
917 2016-03-19 722 1070 786.956522 0.00
918 2016-03-19 1234 902 790.000000 0.25
919 2016-03-19 1461 1566 546.304348 1.75
920 2016-03-19 2346 2300 477.608696 0.50
922 2016-03-19 2398 1238 618.739130 0.00
923 2016-03-19 2667 3016 451.391304 0.75
924 2016-03-19 2891 24902 23.217391 3.75
926 2016-03-19 3373 15211 55.956522 3.00
927 2016-03-20 320 1314 507.260870 0.00
929 2016-03-20 459 985 802.913043 0.00
930 2016-03-20 676 764 802.173913 0.00
931 2016-03-20 722 997 814.565217 0.75
932 2016-03-20 1234 840 805.304348 0.00
933 2016-03-20 1461 1505 538.521739 0.75
934 2016-03-20 2346 2374 474.173913 0.75
935 2016-03-20 2373 1297 646.521739 0.50
936 2016-03-20 2398 1352 680.956522 0.00
937 2016-03-20 2667 2679 407.739130 0.00
938 2016-03-20 2891 29736 23.217391 2.25
940 2016-03-20 3373 15297 57.391304 3.50
941 2016-03-21 320 1569 523.521739 0.25
943 2016-03-21 459 786 747.565217 0.00
944 2016-03-21 676 849 821.391304 0.25
945 2016-03-21 722 2474 823.260870 0.50
946 2016-03-21 1234 973 830.086957 0.00
947 2016-03-21 1461 1522 606.086957 0.50
950 2016-03-21 2346 2116 493.695652 0.50
951 2016-03-21 2373 1069 708.565217 0.50
952 2016-03-21 2398 1135 715.739130 0.00
953 2016-03-21 2667 2648 422.913043 1.50
954 2016-03-21 2891 29122 17.043478 4.25
955 2016-03-21 3373 15809 59.565217 2.00
956 2016-03-22 320 1392 487.869565 0.00
957 2016-03-22 459 1920 850.130435 0.50
958 2016-03-22 676 752 812.000000 0.00
959 2016-03-22 722 12717 676.130435 1.00
960 2016-03-22 1234 896 773.695652 0.00
962 2016-03-22 1461 1192 621.913043 0.00
963 2016-03-22 1490 726 883.347826 0.50
964 2016-03-22 2333 892 867.826087 0.25
965 2016-03-22 2346 2114 528.086957 0.25
966 2016-03-22 2373 885 785.565217 1.25
967 2016-03-22 2398 934 775.521739 0.25
968 2016-03-22 2667 2740 472.521739 2.50
969 2016-03-22 2891 27754 18.913043 3.75
970 2016-03-22 3373 6649 60.086957 0.25
972 2016-03-23 320 1514 486.130435 0.00
973 2016-03-23 459 1469 561.260870 0.25
974 2016-03-23 676 860 837.043478 0.00
975 2016-03-23 722 7450 260.652174 0.75
976 2016-03-23 1234 923 771.304348 0.25
977 2016-03-23 1264 794 783.565217 0.25
978 2016-03-23 1461 1103 700.869565 1.25
979 2016-03-23 1490 940 903.347826 0.00
981 2016-03-23 2346 2628 501.260870 0.00
983 2016-03-23 2398 1021 873.391304 0.00
984 2016-03-23 2667 2653 490.347826 0.25
985 2016-03-23 2891 31568 19.000000 3.25
986 2016-03-23 3373 5017 107.695652 1.25
988 2016-03-24 320 1896 489.869565 0.25
989 2016-03-24 459 1529 561.956522 0.50
990 2016-03-24 676 1173 828.739130 0.00
991 2016-03-24 722 5589 160.217391 0.25
992 2016-03-24 1234 1154 803.000000 0.25
994 2016-03-24 1461 1359 762.956522 1.50
995 2016-03-24 1490 1121 861.652174 1.50
996 2016-03-24 2346 2612 473.391304 0.25
997 2016-03-24 2398 1253 888.695652 0.00
998 2016-03-24 2667 3124 471.000000 0.25
999 2016-03-24 2891 35771 20.521739 5.00
1000 2016-03-24 3373 6328 181.391304 1.50
1002 2016-03-25 320 1904 473.347826 0.00
1003 2016-03-25 459 1733 611.086957 0.00
1004 2016-03-25 676 1114 798.739130 0.00
1005 2016-03-25 722 4362 229.260870 0.50
1006 2016-03-25 1234 1125 812.782609 0.00
1007 2016-03-25 1461 1204 797.608696 1.50
1008 2016-03-25 1490 1009 896.173913 1.00
1009 2016-03-25 2346 3617 485.086957 0.25
1010 2016-03-25 2398 1266 919.304348 0.00
1011 2016-03-25 2667 2958 496.478261 0.00
1012 2016-03-25 2891 35380 21.826087 4.00
1013 2016-03-25 3373 5478 211.782609 1.50
1015 2016-03-26 320 1441 459.695652 0.00
1016 2016-03-26 459 1508 559.478261 0.00
1017 2016-03-26 676 708 794.565217 0.00
1018 2016-03-26 722 2858 282.521739 0.75
1019 2016-03-26 1234 840 800.434783 0.25
1020 2016-03-26 1461 856 825.086957 0.00
1021 2016-03-26 1490 2804 946.826087 0.50
1022 2016-03-26 2346 2284 438.782609 0.25
1023 2016-03-26 2398 859 881.695652 0.00
1024 2016-03-26 2667 2744 503.478261 4.25
1025 2016-03-26 2891 28448 20.869565 3.25
1026 2016-03-26 3373 3938 234.173913 1.25
1028 2016-03-27 320 1417 467.130435 0.25
1029 2016-03-27 459 1104 515.521739 0.00
1030 2016-03-27 676 740 846.000000 0.00
1031 2016-03-27 722 2453 349.304348 1.75
1032 2016-03-27 1234 853 800.478261 0.00
1033 2016-03-27 1461 856 846.521739 0.50
1034 2016-03-27 1490 3839 412.391304 0.50
1035 2016-03-27 2346 2192 464.434783 0.50
1036 2016-03-27 2398 778 906.130435 0.00
1037 2016-03-27 2667 2786 487.391304 3.00
1038 2016-03-27 2891 30854 19.956522 4.25
1039 2016-03-27 3373 3834 248.913043 1.00
1041 2016-03-28 320 1489 483.434783 0.25
1042 2016-03-28 459 946 585.608696 0.00
1043 2016-03-28 676 750 852.304348 0.00
1044 2016-03-28 722 2510 406.826087 6.00
1045 2016-03-28 1234 914 795.347826 0.00
1046 2016-03-28 1461 847 891.434783 0.75
1047 2016-03-28 1490 4023 242.913043 1.00
1048 2016-03-28 2346 3930 424.260870 0.50
1050 2016-03-28 2667 3276 451.608696 3.25
1051 2016-03-28 2891 29605 17.652174 3.00
1052 2016-03-28 3373 3952 247.347826 1.00
1055 2016-03-29 320 1457 493.347826 0.00
1056 2016-03-29 459 1060 668.608696 0.25
1057 2016-03-29 676 963 856.000000 0.00
1058 2016-03-29 722 1390 449.217391 2.50
1059 2016-03-29 1234 998 788.086957 0.50
1060 2016-03-29 1461 924 931.260870 1.25
1061 2016-03-29 1490 1689 233.391304 1.25
1063 2016-03-29 2346 3328 315.000000 0.50
1065 2016-03-29 2667 3006 411.000000 1.00
1066 2016-03-29 2891 30412 16.826087 3.25
1067 2016-03-29 3373 3748 264.130435 1.75
1070 2016-03-30 320 2236 494.391304 0.00
1071 2016-03-30 459 1891 661.130435 0.00
1072 2016-03-30 676 1497 736.043478 0.00
1073 2016-03-30 722 1426 595.478261 1.75
1074 2016-03-30 1234 2628 707.956522 0.00
1076 2016-03-30 1461 1039 945.086957 0.50
1077 2016-03-30 1490 1117 431.086957 1.75
1078 2016-03-30 1874 1364 550.826087 0.00
1079 2016-03-30 2346 2926 337.086957 0.25
1081 2016-03-30 2398 1297 917.565217 0.00
1082 2016-03-30 2667 3257 455.391304 1.75
1085 2016-03-30 3373 3859 269.000000 1.00
1088 2016-03-31 320 2041 445.000000 0.00
1089 2016-03-31 459 1526 506.130435 0.00
1090 2016-03-31 676 1157 622.652174 0.00
1091 2016-03-31 722 1388 673.521739 0.50
1092 2016-03-31 1234 1800 500.913043 0.00
1093 2016-03-31 1264 2433 954.826087 0.75
1094 2016-03-31 1461 1022 905.434783 0.50
1095 2016-03-31 1490 1223 625.434783 0.50
1096 2016-03-31 1874 237 579.869565 0.00
1097 2016-03-31 2346 11099 389.565217 2.00
1098 2016-03-31 2398 1372 918.217391 0.00
1099 2016-03-31 2667 3294 479.130435 1.75
1100 2016-03-31 2891 35903 21.739130 7.75
1101 2016-03-31 3308 777 944.652174 0.25
1102 2016-03-31 3373 3770 312.956522 3.25
newData.avgrating.dtypedtype('float64')
Further we noticed that avg rating column has a lot of values set as 0.00 . We can always replace these null values by a normalize function or we can also replace them by mean. But, replcaing them by either of them in this case, will bias the ML model. We want our model to be trained in such a way, that it can interpret that low ranks have higher sales. For the ease of use, we will only consider avg rank as a factor influencing the sales.
newData1 = newData[['Date','sales','avgrank']]
print(newData1) Date sales avgrank
0 2016-01-01 2412 469.739130
1 2016-01-01 1308 719.173913
2 2016-01-01 2037 603.695652
3 2016-01-01 2052 567.913043
4 2016-01-01 1553 776.826087
5 2016-01-01 2152 518.260870
7 2016-01-01 89289 5.782609
8 2016-01-02 1622 464.043478
9 2016-01-02 1110 754.652174
10 2016-01-02 1553 586.217391
11 2016-01-02 1467 617.782609
12 2016-01-02 1011 727.695652
13 2016-01-02 1722 546.260870
16 2016-01-02 84915 5.826087
17 2016-01-03 1564 508.695652
18 2016-01-03 963 762.434783
19 2016-01-03 1409 560.521739
20 2016-01-03 1371 676.000000
21 2016-01-03 938 829.217391
22 2016-01-03 1662 560.913043
23 2016-01-03 2029 680.086957
24 2016-01-03 61345 5.000000
25 2016-01-04 1609 525.695652
26 2016-01-04 1168 797.217391
27 2016-01-04 1732 573.826087
28 2016-01-04 1421 715.043478
29 2016-01-04 989 840.652174
30 2016-01-04 1081 573.782609
31 2016-01-04 1460 654.565217
32 2016-01-04 58943 6.391304
33 2016-01-05 1594 548.173913
34 2016-01-05 1163 750.869565
35 2016-01-05 1392 563.130435
36 2016-01-05 1482 757.086957
37 2016-01-05 1055 875.173913
38 2016-01-05 984 743.217391
39 2016-01-05 1102 807.869565
40 2016-01-05 62196 10.782609
41 2016-01-06 1883 545.739130
42 2016-01-06 1252 742.173913
44 2016-01-06 1737 706.608696
45 2016-01-06 1227 855.782609
46 2016-01-06 1244 848.260870
49 2016-01-06 67759 10.478261
50 2016-01-07 1978 527.478261
51 2016-01-07 1254 763.217391
53 2016-01-07 1538 701.173913
54 2016-01-07 1125 845.086957
55 2016-01-07 1184 878.869565
56 2016-01-07 956 940.260870
57 2016-01-07 60462 9.434783
58 2016-01-08 1509 483.608696
59 2016-01-08 960 736.043478
61 2016-01-08 1273 702.695652
62 2016-01-08 893 818.695652
63 2016-01-08 876 899.391304
65 2016-01-08 36569 11.608696
66 2016-01-09 1256 502.652174
67 2016-01-09 761 783.478261
68 2016-01-09 867 657.913043
69 2016-01-09 1038 705.956522
70 2016-01-09 808 789.695652
71 2016-01-09 871 872.608696
72 2016-01-09 28872 16.695652
73 2016-01-10 1316 519.652174
74 2016-01-10 775 815.913043
76 2016-01-10 1248 702.260870
78 2016-01-10 795 804.869565
79 2016-01-10 866 844.000000
80 2016-01-10 32229 20.608696
81 2016-01-11 1350 524.173913
82 2016-01-11 744 832.000000
83 2016-01-11 1454 430.869565
84 2016-01-11 1387 651.565217
85 2016-01-11 1008 857.695652
86 2016-01-11 879 824.782609
87 2016-01-11 1505 811.608696
90 2016-01-11 30459 19.086957
91 2016-01-12 1650 525.478261
92 2016-01-12 1071 867.130435
93 2016-01-12 1589 481.826087
94 2016-01-12 1622 588.739130
95 2016-01-12 909 923.565217
96 2016-01-12 946 821.347826
97 2016-01-12 2339 675.521739
98 2016-01-12 2047 774.913043
100 2016-01-12 30254 21.608696
122 2016-01-14 2046 562.434783
123 2016-01-14 1285 824.391304
124 2016-01-14 1893 480.826087
129 2016-01-14 2371 636.304348
134 2016-01-14 49142 19.782609
135 2016-01-15 1813 531.565217
136 2016-01-15 1132 843.869565
137 2016-01-15 1478 556.695652
138 2016-01-15 1613 686.086957
139 2016-01-15 1110 810.347826
140 2016-01-15 2081 637.739130
141 2016-01-15 1769 728.521739
142 2016-01-15 37157 19.173913
143 2016-01-16 1372 493.043478
144 2016-01-16 844 834.304348
146 2016-01-16 1391 685.695652
147 2016-01-16 900 826.260870
148 2016-01-16 1554 639.260870
149 2016-01-16 1443 757.043478
150 2016-01-16 30890 23.956522
151 2016-01-17 1385 519.478261
152 2016-01-17 892 823.521739
153 2016-01-17 977 624.521739
154 2016-01-17 2729 606.086957
155 2016-01-17 919 828.739130
156 2016-01-17 1673 661.000000
157 2016-01-17 1115 787.478261
159 2016-01-17 27467 25.695652
160 2016-01-18 1291 546.608696
161 2016-01-18 777 841.695652
163 2016-01-18 3084 433.347826
164 2016-01-18 804 809.391304
165 2016-01-18 943 678.217391
167 2016-01-18 44326 68.956522
168 2016-01-18 22792 28.304348
169 2016-01-19 1368 545.565217
170 2016-01-19 945 879.913043
171 2016-01-19 1977 733.652174
172 2016-01-19 2754 383.173913
173 2016-01-19 920 835.869565
174 2016-01-19 1094 788.434783
176 2016-01-19 47512 25.739130
177 2016-01-19 29160 32.608696
178 2016-01-20 1746 565.173913
179 2016-01-20 1267 876.347826
181 2016-01-20 3107 390.652174
182 2016-01-20 1096 859.695652
183 2016-01-20 1319 749.478261
184 2016-01-20 1355 766.130435
186 2016-01-20 49850 24.826087
187 2016-01-20 36121 33.000000
188 2016-01-21 1828 555.695652
189 2016-01-21 1124 805.956522
190 2016-01-21 1923 401.782609
191 2016-01-21 2897 407.304348
192 2016-01-21 1220 848.739130
193 2016-01-21 1399 714.043478
194 2016-01-21 1582 854.217391
195 2016-01-21 1958 838.695652
196 2016-01-21 45371 27.652174
197 2016-01-21 34248 35.478261
198 2016-01-22 1232 514.913043
199 2016-01-22 727 821.478261
200 2016-01-22 1532 462.260870
201 2016-01-22 2090 431.043478
202 2016-01-22 842 768.130435
203 2016-01-22 1043 692.086957
204 2016-01-22 1284 892.043478
205 2016-01-22 1654 667.043478
206 2016-01-22 28863 29.000000
207 2016-01-22 21218 35.913043
208 2016-01-23 1167 502.173913
209 2016-01-23 798 847.739130
210 2016-01-23 1198 479.521739
211 2016-01-23 2135 433.043478
212 2016-01-23 775 769.173913
213 2016-01-23 856 679.826087
214 2016-01-23 1276 794.826087
215 2016-01-23 1192 629.391304
218 2016-01-24 1189 532.695652
219 2016-01-24 708 862.695652
220 2016-01-24 2028 474.652174
221 2016-01-24 1781 450.130435
223 2016-01-24 794 802.521739
224 2016-01-24 886 737.347826
225 2016-01-24 1120 750.782609
226 2016-01-24 1168 645.434783
227 2016-01-24 21527 36.782609
228 2016-01-24 18949 32.826087
229 2016-01-25 1250 539.304348
230 2016-01-25 779 894.130435
231 2016-01-25 1336 423.304348
232 2016-01-25 1703 501.217391
233 2016-01-25 1019 928.956522
234 2016-01-25 791 870.000000
235 2016-01-25 935 732.086957
236 2016-01-25 1087 811.565217
237 2016-01-25 1259 726.565217
238 2016-01-25 16278 46.739130
239 2016-01-25 20721 36.869565
240 2016-01-26 1368 545.782609
241 2016-01-26 924 902.130435
242 2016-01-26 1431 520.347826
243 2016-01-26 2316 507.782609
244 2016-01-26 949 932.826087
245 2016-01-26 931 843.956522
246 2016-01-26 1029 723.521739
248 2016-01-26 1204 702.347826
249 2016-01-26 16522 63.478261
250 2016-01-26 27454 33.217391
251 2016-01-27 1807 553.434783
252 2016-01-27 1317 874.086957
253 2016-01-27 2079 520.347826
254 2016-01-27 2487 482.565217
256 2016-01-27 1157 853.130435
257 2016-01-27 1213 757.086957
259 2016-01-27 1758 746.652174
260 2016-01-27 18981 77.739130
261 2016-01-27 34159 31.391304
262 2016-01-28 1639 554.608696
263 2016-01-28 1188 799.391304
264 2016-01-28 2361 514.521739
265 2016-01-28 2116 505.304348
266 2016-01-28 1147 840.000000
267 2016-01-28 1321 781.956522
269 2016-01-28 1935 726.347826
270 2016-01-28 17933 85.260870
271 2016-01-28 34537 30.826087
272 2016-01-29 1240 537.173913
273 2016-01-29 703 816.304348
274 2016-01-29 1480 456.391304
275 2016-01-29 1502 540.739130
276 2016-01-29 827 842.521739
277 2016-01-29 863 748.565217
278 2016-01-29 7055 164.739130
279 2016-01-29 386 736.652174
280 2016-01-29 13235 88.956522
281 2016-01-29 20230 30.913043
282 2016-01-30 1175 529.652174
283 2016-01-30 667 868.956522
284 2016-01-30 1409 467.521739
285 2016-01-30 1727 564.826087
286 2016-01-30 797 824.173913
287 2016-01-30 1197 789.565217
289 2016-01-30 7829 138.565217
291 2016-01-30 13021 87.304348
292 2016-01-30 16766 38.478261
294 2016-01-31 1149 532.739130
295 2016-01-31 711 896.478261
296 2016-01-31 1207 495.521739
297 2016-01-31 2059 475.086957
298 2016-01-31 790 781.086957
299 2016-01-31 739 676.043478
301 2016-01-31 6435 128.565217
302 2016-01-31 11656 91.043478
303 2016-01-31 15648 45.173913
304 2016-02-01 1270 547.086957
305 2016-02-01 773 884.434783
306 2016-02-01 1271 525.869565
307 2016-02-01 1649 473.521739
309 2016-02-01 928 777.652174
310 2016-02-01 751 772.565217
312 2016-02-01 6630 134.260870
313 2016-02-01 8485 105.260870
314 2016-02-01 16267 50.565217
316 2016-02-02 1435 549.434783
317 2016-02-02 1003 873.434783
318 2016-02-02 1181 588.391304
319 2016-02-02 1770 505.000000
320 2016-02-02 1080 747.652174
323 2016-02-02 4731 155.739130
324 2016-02-02 8677 145.565217
325 2016-02-02 18581 56.521739
327 2016-02-03 1858 563.478261
328 2016-02-03 1419 817.521739
329 2016-02-03 1409 630.000000
330 2016-02-03 2330 540.521739
331 2016-02-03 1361 768.043478
333 2016-02-03 2569 765.043478
334 2016-02-03 6081 197.130435
335 2016-02-03 9948 163.565217
336 2016-02-03 25123 57.173913
339 2016-02-04 1869 537.782609
340 2016-02-04 1312 813.956522
341 2016-02-04 1678 648.086957
342 2016-02-04 2164 550.304348
343 2016-02-04 1254 737.217391
345 2016-02-04 3304 621.826087
346 2016-02-04 4468 228.086957
347 2016-02-04 9212 183.956522
348 2016-02-04 23394 59.565217
349 2016-02-04 6235 262.086957
351 2016-02-05 1320 520.086957
352 2016-02-05 846 800.695652
353 2016-02-05 1293 616.869565
354 2016-02-05 1449 561.782609
355 2016-02-05 925 754.173913
356 2016-02-05 2616 541.304348
357 2016-02-05 2302 293.695652
358 2016-02-05 6666 186.130435
359 2016-02-05 14004 60.043478
360 2016-02-05 3760 248.391304
362 2016-02-06 1280 533.695652
363 2016-02-06 769 808.304348
364 2016-02-06 1014 612.695652
365 2016-02-06 1769 557.695652
366 2016-02-06 925 759.521739
367 2016-02-06 2343 530.391304
368 2016-02-06 1973 386.956522
369 2016-02-06 6154 177.565217
370 2016-02-06 13272 65.478261
371 2016-02-06 3408 295.260870
372 2016-02-06 3552 364.260870
373 2016-02-07 1439 536.434783
374 2016-02-07 929 850.434783
375 2016-02-07 979 654.217391
376 2016-02-07 1663 515.086957
377 2016-02-07 989 789.565217
378 2016-02-07 2189 521.739130
379 2016-02-07 1938 458.608696
380 2016-02-07 6039 181.217391
381 2016-02-07 12973 69.869565
382 2016-02-07 3955 334.391304
383 2016-02-07 3062 379.000000
384 2016-02-08 1359 530.869565
385 2016-02-08 815 838.304348
386 2016-02-08 1439 692.521739
387 2016-02-08 1667 554.956522
388 2016-02-08 927 768.913043
389 2016-02-08 2127 516.391304
390 2016-02-08 1618 515.260870
391 2016-02-08 5211 193.304348
392 2016-02-08 11992 78.173913
393 2016-02-08 1317 335.000000
394 2016-02-08 2002 435.304348
395 2016-02-09 1735 521.478261
396 2016-02-09 1020 816.739130
397 2016-02-09 1465 580.739130
398 2016-02-09 2006 607.391304
400 2016-02-09 1088 752.304348
401 2016-02-09 2027 548.434783
402 2016-02-09 1568 558.826087
403 2016-02-09 5879 210.043478
404 2016-02-09 15179 78.130435
405 2016-02-09 556 606.913043
406 2016-02-09 2652 537.695652
407 2016-02-10 2212 527.739130
408 2016-02-10 1385 764.434783
409 2016-02-10 1697 581.565217
410 2016-02-10 2673 575.869565
411 2016-02-10 1448 762.478261
412 2016-02-10 3024 627.956522
413 2016-02-10 1736 650.608696
414 2016-02-10 6547 229.260870
415 2016-02-10 20504 80.869565
418 2016-02-10 2061 569.391304
419 2016-02-11 2064 481.347826
420 2016-02-11 1238 785.565217
421 2016-02-11 1760 622.608696
422 2016-02-11 2236 552.000000
423 2016-02-11 1381 714.173913
424 2016-02-11 3513 602.695652
425 2016-02-11 1719 707.347826
426 2016-02-11 6110 261.956522
427 2016-02-11 17485 76.695652
429 2016-02-11 1916 794.652174
430 2016-02-12 1449 467.000000
431 2016-02-12 791 825.173913
432 2016-02-12 1235 604.130435
433 2016-02-12 1489 593.043478
434 2016-02-12 1001 709.000000
435 2016-02-12 2534 523.347826
436 2016-02-12 1232 723.000000
437 2016-02-12 4729 272.521739
438 2016-02-12 11147 79.086957
442 2016-02-13 1342 487.478261
443 2016-02-13 702 858.956522
444 2016-02-13 1728 563.434783
445 2016-02-13 1380 606.695652
447 2016-02-13 1009 678.956522
448 2016-02-13 2332 462.000000
450 2016-02-13 1271 739.304348
451 2016-02-13 4395 261.391304
452 2016-02-13 9598 82.260870
454 2016-02-14 2928 223.304348
455 2016-02-14 1280 506.347826
456 2016-02-14 674 904.260870
457 2016-02-14 949 473.565217
458 2016-02-14 1353 625.956522
459 2016-02-14 746 924.086957
460 2016-02-14 877 684.695652
461 2016-02-14 1954 451.130435
462 2016-02-14 826 742.739130
463 2016-02-14 1090 701.347826
464 2016-02-14 4079 261.043478
465 2016-02-14 10271 87.956522
468 2016-02-15 2029 278.826087
469 2016-02-15 1430 485.391304
470 2016-02-15 768 902.086957
471 2016-02-15 940 562.173913
472 2016-02-15 1441 606.608696
474 2016-02-15 969 692.000000
476 2016-02-15 2134 479.173913
478 2016-02-15 1316 726.478261
479 2016-02-15 4924 253.956522
480 2016-02-15 11795 78.000000
483 2016-02-16 1851 415.913043
484 2016-02-16 1582 480.956522
485 2016-02-16 1000 888.434783
486 2016-02-16 1409 644.608696
487 2016-02-16 1997 598.000000
488 2016-02-16 1079 726.913043
489 2016-02-16 1562 867.652174
490 2016-02-16 2062 503.173913
491 2016-02-16 1478 710.826087
492 2016-02-16 5353 251.782609
493 2016-02-16 15537 75.478261
494 2016-02-17 1723 561.956522
495 2016-02-17 1919 503.304348
496 2016-02-17 1307 865.000000
497 2016-02-17 1661 582.913043
498 2016-02-17 2217 562.391304
499 2016-02-17 1327 750.565217
500 2016-02-17 1960 807.521739
501 2016-02-17 2706 569.739130
502 2016-02-17 1865 747.043478
503 2016-02-17 8867 265.608696
504 2016-02-17 20417 74.652174
506 2016-02-18 1419 705.086957
507 2016-02-18 2025 506.782609
508 2016-02-18 1293 802.217391
509 2016-02-18 1821 584.217391
510 2016-02-18 2385 574.869565
511 2016-02-18 1310 771.304348
512 2016-02-18 1734 835.739130
513 2016-02-18 3556 552.391304
514 2016-02-18 2286 733.695652
515 2016-02-18 35279 184.565217
516 2016-02-18 20284 72.043478
518 2016-02-19 1357 798.869565
519 2016-02-19 1560 505.173913
520 2016-02-19 1021 810.000000
521 2016-02-19 715 628.000000
522 2016-02-19 1969 533.086957
523 2016-02-19 1170 775.478261
524 2016-02-19 1476 859.521739
525 2016-02-19 3099 496.043478
526 2016-02-19 1933 668.304348
527 2016-02-19 16330 78.826087
528 2016-02-19 19032 71.130435
531 2016-02-20 1302 513.260870
532 2016-02-20 856 844.695652
533 2016-02-20 1271 794.260870
534 2016-02-20 1555 542.043478
535 2016-02-20 946 780.913043
536 2016-02-20 1163 839.000000
537 2016-02-20 3295 443.869565
538 2016-02-20 1310 670.000000
539 2016-02-20 10265 98.652174
540 2016-02-20 14375 64.173913
542 2016-02-21 1357 508.869565
543 2016-02-21 799 826.521739
544 2016-02-21 1187 679.913043
545 2016-02-21 1413 560.130435
546 2016-02-21 972 771.695652
547 2016-02-21 1138 880.826087
548 2016-02-21 3777 381.652174
550 2016-02-21 1256 706.695652
551 2016-02-21 8114 119.260870
552 2016-02-21 14902 63.478261
554 2016-02-22 1396 508.086957
555 2016-02-22 789 821.217391
556 2016-02-22 1086 623.347826
557 2016-02-22 1486 600.391304
558 2016-02-22 1024 735.217391
559 2016-02-22 806 882.956522
560 2016-02-22 5447 321.782609
561 2016-02-22 820 657.739130
562 2016-02-22 1297 741.391304
563 2016-02-22 7352 150.478261
564 2016-02-22 18591 60.608696
567 2016-02-23 1654 499.739130
568 2016-02-23 1059 848.130435
569 2016-02-23 1381 589.217391
570 2016-02-23 1763 610.130435
572 2016-02-23 1149 717.173913
574 2016-02-23 3896 349.652174
576 2016-02-23 1469 736.043478
577 2016-02-23 7706 163.869565
578 2016-02-23 21589 45.000000
581 2016-02-24 2128 492.913043
582 2016-02-24 1373 777.217391
583 2016-02-24 1614 581.608696
585 2016-02-24 2165 589.608696
586 2016-02-24 1373 706.869565
588 2016-02-24 3034 431.347826
589 2016-02-24 2178 718.782609
590 2016-02-24 8076 187.347826
592 2016-02-24 29501 40.173913
593 2016-02-25 1936 477.739130
594 2016-02-25 1227 760.391304
595 2016-02-25 1717 638.000000
596 2016-02-25 983 933.434783
597 2016-02-25 1981 595.086957
598 2016-02-25 1319 734.652174
600 2016-02-25 3048 529.739130
601 2016-02-25 1689 693.478261
602 2016-02-25 5546 221.956522
603 2016-02-25 1647 619.304348
604 2016-02-25 31090 35.304348
605 2016-02-26 1480 468.173913
606 2016-02-26 811 774.130435
607 2016-02-26 1055 620.739130
608 2016-02-26 627 925.173913
609 2016-02-26 1183 612.086957
610 2016-02-26 962 725.130435
612 2016-02-26 2712 509.913043
613 2016-02-26 1020 762.695652
614 2016-02-26 4138 266.304348
615 2016-02-26 176 648.521739
616 2016-02-26 16154 34.521739
618 2016-02-27 1458 422.956522
619 2016-02-27 700 785.695652
620 2016-02-27 865 646.869565
621 2016-02-27 563 934.956522
622 2016-02-27 1314 668.826087
624 2016-02-27 892 696.913043
625 2016-02-27 2770 426.565217
626 2016-02-27 1199 781.130435
627 2016-02-27 4485 271.695652
629 2016-02-27 15148 41.130435
632 2016-02-28 1430 414.217391
633 2016-02-28 768 803.000000
634 2016-02-28 1364 670.347826
636 2016-02-28 1411 618.869565
638 2016-02-28 951 708.826087
639 2016-02-28 2600 374.608696
640 2016-02-28 1438 758.260870
641 2016-02-28 4460 260.695652
642 2016-02-28 15738 43.478261
643 2016-02-28 1047 880.260870
645 2016-03-01 1586 423.913043
646 2016-03-01 853 796.217391
647 2016-03-01 1590 560.869565
648 2016-03-01 662 957.086957
649 2016-03-01 3075 571.608696
651 2016-03-01 979 683.739130
653 2016-03-01 1548 398.913043
654 2016-03-01 1401 706.304348
655 2016-03-01 5233 245.565217
656 2016-03-01 19151 39.782609
657 2016-03-01 1200 745.434783
659 2016-03-02 2041 417.260870
660 2016-03-02 1012 813.565217
661 2016-03-02 1786 506.043478
662 2016-03-02 823 928.217391
663 2016-03-02 4436 431.434783
664 2016-03-02 1114 681.217391
667 2016-03-02 4691 477.260870
668 2016-03-02 1542 537.043478
669 2016-03-02 2221 635.086957
670 2016-03-02 5296 223.304348
671 2016-03-02 25378 34.826087
672 2016-03-02 1252 663.565217
673 2016-03-03 2322 393.304348
674 2016-03-03 1277 805.869565
675 2016-03-03 1508 524.391304
676 2016-03-03 1049 924.826087
677 2016-03-03 5262 371.695652
678 2016-03-03 1294 716.826087
679 2016-03-03 1317 872.565217
680 2016-03-03 6659 327.434783
681 2016-03-03 1706 683.217391
682 2016-03-03 2198 505.304348
683 2016-03-03 5575 253.652174
684 2016-03-03 33408 29.565217
685 2016-03-03 1982 662.434783
687 2016-03-04 2084 404.826087
688 2016-03-04 1123 801.304348
689 2016-03-04 1720 594.521739
690 2016-03-04 990 915.913043
691 2016-03-04 4766 364.130435
692 2016-03-04 1396 752.217391
693 2016-03-04 1115 904.434783
694 2016-03-04 6417 253.739130
695 2016-03-04 1925 776.739130
696 2016-03-04 1691 573.000000
698 2016-03-04 5401 291.782609
699 2016-03-04 32011 30.565217
700 2016-03-04 2568 612.521739
701 2016-03-05 1581 405.043478
702 2016-03-05 664 823.173913
703 2016-03-05 1888 566.000000
704 2016-03-05 631 892.000000
705 2016-03-05 3108 368.608696
707 2016-03-05 3348 572.695652
708 2016-03-05 1131 890.869565
709 2016-03-05 4743 255.695652
710 2016-03-05 1862 716.565217
711 2016-03-05 937 695.913043
713 2016-03-05 3862 293.869565
714 2016-03-05 32532 28.869565
715 2016-03-05 2426 496.521739
717 2016-03-06 1527 410.434783
718 2016-03-06 757 886.260870
719 2016-03-06 1526 437.260870
720 2016-03-06 786 832.913043
721 2016-03-06 3066 382.000000
722 2016-03-06 928 922.652174
723 2016-03-06 1710 350.260870
724 2016-03-06 1391 779.956522
726 2016-03-06 2689 246.260870
727 2016-03-06 1827 603.695652
728 2016-03-06 990 778.913043
729 2016-03-06 4908 289.695652
730 2016-03-06 33458 17.130435
731 2016-03-06 1779 412.652174
733 2016-03-07 1603 431.521739
734 2016-03-07 798 928.130435
735 2016-03-07 1207 496.043478
736 2016-03-07 1008 780.260870
737 2016-03-07 3151 398.304348
739 2016-03-07 1368 425.695652
740 2016-03-07 1529 659.130435
741 2016-03-07 1731 354.260870
742 2016-03-07 2077 595.000000
743 2016-03-07 1188 812.000000
744 2016-03-07 4436 257.869565
745 2016-03-07 34919 14.521739
746 2016-03-07 1640 447.478261
747 2016-03-08 1469 422.608696
748 2016-03-08 711 898.434783
749 2016-03-08 1096 612.956522
750 2016-03-08 757 702.347826
751 2016-03-08 2009 415.086957
752 2016-03-08 1082 553.043478
753 2016-03-08 1259 593.217391
754 2016-03-08 2015 562.565217
755 2016-03-08 2057 536.130435
756 2016-03-08 1143 768.130435
757 2016-03-08 3709 267.260870
758 2016-03-08 33293 13.652174
759 2016-03-08 1551 504.565217
760 2016-03-09 1639 436.086957
761 2016-03-09 957 930.086957
762 2016-03-09 1567 623.260870
763 2016-03-09 859 779.782609
764 2016-03-09 1851 507.869565
765 2016-03-09 1259 628.869565
767 2016-03-09 1520 613.260870
769 2016-03-09 2084 595.000000
770 2016-03-09 1915 497.652174
771 2016-03-09 1290 829.478261
772 2016-03-09 3816 303.826087
773 2016-03-09 35441 13.565217
774 2016-03-09 2183 473.173913
775 2016-03-10 1919 475.391304
776 2016-03-10 1280 902.000000
777 2016-03-10 1869 532.521739
778 2016-03-10 1209 801.130435
779 2016-03-10 2006 518.043478
780 2016-03-10 1477 645.000000
781 2016-03-10 998 877.217391
782 2016-03-10 1766 607.434783
783 2016-03-10 19010 135.608696
784 2016-03-10 2478 618.000000
785 2016-03-10 2616 571.478261
786 2016-03-10 1957 774.956522
787 2016-03-10 4539 333.391304
788 2016-03-10 41386 15.826087
789 2016-03-10 2946 408.347826
805 2016-03-12 1395 481.565217
806 2016-03-12 895 906.260870
807 2016-03-12 1154 575.956522
808 2016-03-12 919 752.565217
809 2016-03-12 1231 650.043478
810 2016-03-12 874 730.173913
812 2016-03-12 1602 657.086957
813 2016-03-12 16656 45.130435
814 2016-03-12 1771 557.260870
815 2016-03-12 2661 457.869565
816 2016-03-12 993 873.608696
817 2016-03-12 3257 365.086957
818 2016-03-12 28069 23.000000
819 2016-03-12 1447 404.913043
820 2016-03-13 1772 477.130435
821 2016-03-13 833 891.086957
822 2016-03-13 1127 615.434783
823 2016-03-13 1036 747.739130
824 2016-03-13 1188 691.826087
825 2016-03-13 898 764.565217
826 2016-03-13 2082 442.956522
827 2016-03-13 1502 599.130435
828 2016-03-13 4107 52.217391
829 2016-03-13 1385 595.260870
830 2016-03-13 2750 439.130435
831 2016-03-13 1027 931.608696
832 2016-03-13 2941 371.608696
833 2016-03-13 28991 22.826087
834 2016-03-13 872 531.130435
835 2016-03-14 1528 431.565217
836 2016-03-14 738 921.869565
837 2016-03-14 1084 654.652174
838 2016-03-14 871 783.043478
839 2016-03-14 1163 734.521739
840 2016-03-14 880 771.608696
841 2016-03-14 1433 507.608696
842 2016-03-14 1039 607.478261
844 2016-03-14 1761 116.565217
845 2016-03-14 1777 683.217391
846 2016-03-14 2917 411.739130
847 2016-03-14 909 891.173913
848 2016-03-14 3078 405.478261
849 2016-03-14 27801 20.782609
850 2016-03-14 815 710.913043
851 2016-03-15 1614 416.173913
852 2016-03-15 758 931.260870
853 2016-03-15 873 689.608696
854 2016-03-15 743 778.521739
855 2016-03-15 1139 751.695652
856 2016-03-15 953 764.782609
857 2016-03-15 1653 639.478261
858 2016-03-15 787 708.260870
859 2016-03-15 443 622.652174
860 2016-03-15 472 268.913043
861 2016-03-15 1107 634.000000
862 2016-03-15 2533 398.782609
863 2016-03-15 1234 912.608696
864 2016-03-15 3078 385.000000
865 2016-03-15 27124 18.869565
866 2016-03-15 674 854.347826
868 2016-03-16 1639 432.000000
869 2016-03-16 1088 981.000000
870 2016-03-16 1643 816.000000
871 2016-03-16 1036 822.000000
872 2016-03-16 1288 778.000000
873 2016-03-16 929 757.000000
874 2016-03-16 1993 632.000000
875 2016-03-16 779 824.000000
876 2016-03-16 151 826.000000
877 2016-03-16 48 463.000000
878 2016-03-16 913 725.000000
879 2016-03-16 2490 429.000000
880 2016-03-16 2112 807.000000
881 2016-03-16 3049 402.000000
882 2016-03-16 30309 20.000000
883 2016-03-16 704 957.000000
884 2016-03-16 23134 121.000000
885 2016-03-17 1826 471.521739
886 2016-03-17 1266 882.695652
887 2016-03-17 1674 632.869565
888 2016-03-17 1286 758.347826
889 2016-03-17 1518 778.347826
890 2016-03-17 1170 786.434783
891 2016-03-17 2531 593.826087
892 2016-03-17 1025 929.652174
895 2016-03-17 2808 477.695652
896 2016-03-17 1842 602.391304
897 2016-03-17 3495 426.434783
898 2016-03-17 34961 20.478261
899 2016-03-17 26072 70.565217
900 2016-03-18 1680 499.434783
901 2016-03-18 1114 894.434783
902 2016-03-18 1160 569.391304
903 2016-03-18 1154 719.695652
904 2016-03-18 1367 788.956522
905 2016-03-18 1143 811.913043
906 2016-03-18 2260 539.130435
908 2016-03-18 2897 522.086957
909 2016-03-18 1793 636.826087
910 2016-03-18 3170 468.000000
911 2016-03-18 32913 23.652174
912 2016-03-18 20929 49.913043
913 2016-03-19 1325 513.260870
915 2016-03-19 698 705.956522
916 2016-03-19 661 746.869565
917 2016-03-19 1070 786.956522
918 2016-03-19 902 790.000000
919 2016-03-19 1566 546.304348
920 2016-03-19 2300 477.608696
922 2016-03-19 1238 618.739130
923 2016-03-19 3016 451.391304
924 2016-03-19 24902 23.217391
926 2016-03-19 15211 55.956522
927 2016-03-20 1314 507.260870
929 2016-03-20 985 802.913043
930 2016-03-20 764 802.173913
931 2016-03-20 997 814.565217
932 2016-03-20 840 805.304348
933 2016-03-20 1505 538.521739
934 2016-03-20 2374 474.173913
935 2016-03-20 1297 646.521739
936 2016-03-20 1352 680.956522
937 2016-03-20 2679 407.739130
938 2016-03-20 29736 23.217391
940 2016-03-20 15297 57.391304
941 2016-03-21 1569 523.521739
943 2016-03-21 786 747.565217
944 2016-03-21 849 821.391304
945 2016-03-21 2474 823.260870
946 2016-03-21 973 830.086957
947 2016-03-21 1522 606.086957
950 2016-03-21 2116 493.695652
951 2016-03-21 1069 708.565217
952 2016-03-21 1135 715.739130
953 2016-03-21 2648 422.913043
954 2016-03-21 29122 17.043478
955 2016-03-21 15809 59.565217
956 2016-03-22 1392 487.869565
957 2016-03-22 1920 850.130435
958 2016-03-22 752 812.000000
959 2016-03-22 12717 676.130435
960 2016-03-22 896 773.695652
962 2016-03-22 1192 621.913043
963 2016-03-22 726 883.347826
964 2016-03-22 892 867.826087
965 2016-03-22 2114 528.086957
966 2016-03-22 885 785.565217
967 2016-03-22 934 775.521739
968 2016-03-22 2740 472.521739
969 2016-03-22 27754 18.913043
970 2016-03-22 6649 60.086957
972 2016-03-23 1514 486.130435
973 2016-03-23 1469 561.260870
974 2016-03-23 860 837.043478
975 2016-03-23 7450 260.652174
976 2016-03-23 923 771.304348
977 2016-03-23 794 783.565217
978 2016-03-23 1103 700.869565
979 2016-03-23 940 903.347826
981 2016-03-23 2628 501.260870
983 2016-03-23 1021 873.391304
984 2016-03-23 2653 490.347826
985 2016-03-23 31568 19.000000
986 2016-03-23 5017 107.695652
988 2016-03-24 1896 489.869565
989 2016-03-24 1529 561.956522
990 2016-03-24 1173 828.739130
991 2016-03-24 5589 160.217391
992 2016-03-24 1154 803.000000
994 2016-03-24 1359 762.956522
995 2016-03-24 1121 861.652174
996 2016-03-24 2612 473.391304
997 2016-03-24 1253 888.695652
998 2016-03-24 3124 471.000000
999 2016-03-24 35771 20.521739
1000 2016-03-24 6328 181.391304
1002 2016-03-25 1904 473.347826
1003 2016-03-25 1733 611.086957
1004 2016-03-25 1114 798.739130
1005 2016-03-25 4362 229.260870
1006 2016-03-25 1125 812.782609
1007 2016-03-25 1204 797.608696
1008 2016-03-25 1009 896.173913
1009 2016-03-25 3617 485.086957
1010 2016-03-25 1266 919.304348
1011 2016-03-25 2958 496.478261
1012 2016-03-25 35380 21.826087
1013 2016-03-25 5478 211.782609
1015 2016-03-26 1441 459.695652
1016 2016-03-26 1508 559.478261
1017 2016-03-26 708 794.565217
1018 2016-03-26 2858 282.521739
1019 2016-03-26 840 800.434783
1020 2016-03-26 856 825.086957
1021 2016-03-26 2804 946.826087
1022 2016-03-26 2284 438.782609
1023 2016-03-26 859 881.695652
1024 2016-03-26 2744 503.478261
1025 2016-03-26 28448 20.869565
1026 2016-03-26 3938 234.173913
1028 2016-03-27 1417 467.130435
1029 2016-03-27 1104 515.521739
1030 2016-03-27 740 846.000000
1031 2016-03-27 2453 349.304348
1032 2016-03-27 853 800.478261
1033 2016-03-27 856 846.521739
1034 2016-03-27 3839 412.391304
1035 2016-03-27 2192 464.434783
1036 2016-03-27 778 906.130435
1037 2016-03-27 2786 487.391304
1038 2016-03-27 30854 19.956522
1039 2016-03-27 3834 248.913043
1041 2016-03-28 1489 483.434783
1042 2016-03-28 946 585.608696
1043 2016-03-28 750 852.304348
1044 2016-03-28 2510 406.826087
1045 2016-03-28 914 795.347826
1046 2016-03-28 847 891.434783
1047 2016-03-28 4023 242.913043
1048 2016-03-28 3930 424.260870
1050 2016-03-28 3276 451.608696
1051 2016-03-28 29605 17.652174
1052 2016-03-28 3952 247.347826
1055 2016-03-29 1457 493.347826
1056 2016-03-29 1060 668.608696
1057 2016-03-29 963 856.000000
1058 2016-03-29 1390 449.217391
1059 2016-03-29 998 788.086957
1060 2016-03-29 924 931.260870
1061 2016-03-29 1689 233.391304
1063 2016-03-29 3328 315.000000
1065 2016-03-29 3006 411.000000
1066 2016-03-29 30412 16.826087
1067 2016-03-29 3748 264.130435
1070 2016-03-30 2236 494.391304
1071 2016-03-30 1891 661.130435
1072 2016-03-30 1497 736.043478
1073 2016-03-30 1426 595.478261
1074 2016-03-30 2628 707.956522
1076 2016-03-30 1039 945.086957
1077 2016-03-30 1117 431.086957
1078 2016-03-30 1364 550.826087
1079 2016-03-30 2926 337.086957
1081 2016-03-30 1297 917.565217
1082 2016-03-30 3257 455.391304
1085 2016-03-30 3859 269.000000
1088 2016-03-31 2041 445.000000
1089 2016-03-31 1526 506.130435
1090 2016-03-31 1157 622.652174
1091 2016-03-31 1388 673.521739
1092 2016-03-31 1800 500.913043
1093 2016-03-31 2433 954.826087
1094 2016-03-31 1022 905.434783
1095 2016-03-31 1223 625.434783
1096 2016-03-31 237 579.869565
1097 2016-03-31 11099 389.565217
1098 2016-03-31 1372 918.217391
1099 2016-03-31 3294 479.130435
1100 2016-03-31 35903 21.739130
1101 2016-03-31 777 944.652174
1102 2016-03-31 3770 312.956522
newData1.columnsIndex(['Date', 'sales', 'avgrank'], dtype='object')
newData1.Date.dtypedtype('O')
pd.options.mode.chained_assignment = None # default='warn'As we can see that there is a date column in our dataframe but it is of type 'O' . So we will convert the data type and date & time format so that it can be interpreted as a time series data by our model. We will also change the index of the dataframe to Date.
newData1['Date'] = pd.to_datetime(newData1['Date'] , format = '%Y/%m/%d')
newData2 = newData1.drop(['Date'], axis=1)
newData2.index = newData.Dateprint(newData2) sales avgrank
Date
2016-01-01 2412 469.739130
2016-01-01 1308 719.173913
2016-01-01 2037 603.695652
2016-01-01 2052 567.913043
2016-01-01 1553 776.826087
2016-01-01 2152 518.260870
2016-01-01 89289 5.782609
2016-01-02 1622 464.043478
2016-01-02 1110 754.652174
2016-01-02 1553 586.217391
2016-01-02 1467 617.782609
2016-01-02 1011 727.695652
2016-01-02 1722 546.260870
2016-01-02 84915 5.826087
2016-01-03 1564 508.695652
2016-01-03 963 762.434783
2016-01-03 1409 560.521739
2016-01-03 1371 676.000000
2016-01-03 938 829.217391
2016-01-03 1662 560.913043
2016-01-03 2029 680.086957
2016-01-03 61345 5.000000
2016-01-04 1609 525.695652
2016-01-04 1168 797.217391
2016-01-04 1732 573.826087
2016-01-04 1421 715.043478
2016-01-04 989 840.652174
2016-01-04 1081 573.782609
2016-01-04 1460 654.565217
2016-01-04 58943 6.391304
2016-01-05 1594 548.173913
2016-01-05 1163 750.869565
2016-01-05 1392 563.130435
2016-01-05 1482 757.086957
2016-01-05 1055 875.173913
2016-01-05 984 743.217391
2016-01-05 1102 807.869565
2016-01-05 62196 10.782609
2016-01-06 1883 545.739130
2016-01-06 1252 742.173913
2016-01-06 1737 706.608696
2016-01-06 1227 855.782609
2016-01-06 1244 848.260870
2016-01-06 67759 10.478261
2016-01-07 1978 527.478261
2016-01-07 1254 763.217391
2016-01-07 1538 701.173913
2016-01-07 1125 845.086957
2016-01-07 1184 878.869565
2016-01-07 956 940.260870
2016-01-07 60462 9.434783
2016-01-08 1509 483.608696
2016-01-08 960 736.043478
2016-01-08 1273 702.695652
2016-01-08 893 818.695652
2016-01-08 876 899.391304
2016-01-08 36569 11.608696
2016-01-09 1256 502.652174
2016-01-09 761 783.478261
2016-01-09 867 657.913043
2016-01-09 1038 705.956522
2016-01-09 808 789.695652
2016-01-09 871 872.608696
2016-01-09 28872 16.695652
2016-01-10 1316 519.652174
2016-01-10 775 815.913043
2016-01-10 1248 702.260870
2016-01-10 795 804.869565
2016-01-10 866 844.000000
2016-01-10 32229 20.608696
2016-01-11 1350 524.173913
2016-01-11 744 832.000000
2016-01-11 1454 430.869565
2016-01-11 1387 651.565217
2016-01-11 1008 857.695652
2016-01-11 879 824.782609
2016-01-11 1505 811.608696
2016-01-11 30459 19.086957
2016-01-12 1650 525.478261
2016-01-12 1071 867.130435
2016-01-12 1589 481.826087
2016-01-12 1622 588.739130
2016-01-12 909 923.565217
2016-01-12 946 821.347826
2016-01-12 2339 675.521739
2016-01-12 2047 774.913043
2016-01-12 30254 21.608696
2016-01-14 2046 562.434783
2016-01-14 1285 824.391304
2016-01-14 1893 480.826087
2016-01-14 2371 636.304348
2016-01-14 49142 19.782609
2016-01-15 1813 531.565217
2016-01-15 1132 843.869565
2016-01-15 1478 556.695652
2016-01-15 1613 686.086957
2016-01-15 1110 810.347826
2016-01-15 2081 637.739130
2016-01-15 1769 728.521739
2016-01-15 37157 19.173913
2016-01-16 1372 493.043478
2016-01-16 844 834.304348
2016-01-16 1391 685.695652
2016-01-16 900 826.260870
2016-01-16 1554 639.260870
2016-01-16 1443 757.043478
2016-01-16 30890 23.956522
2016-01-17 1385 519.478261
2016-01-17 892 823.521739
2016-01-17 977 624.521739
2016-01-17 2729 606.086957
2016-01-17 919 828.739130
2016-01-17 1673 661.000000
2016-01-17 1115 787.478261
2016-01-17 27467 25.695652
2016-01-18 1291 546.608696
2016-01-18 777 841.695652
2016-01-18 3084 433.347826
2016-01-18 804 809.391304
2016-01-18 943 678.217391
2016-01-18 44326 68.956522
2016-01-18 22792 28.304348
2016-01-19 1368 545.565217
2016-01-19 945 879.913043
2016-01-19 1977 733.652174
2016-01-19 2754 383.173913
2016-01-19 920 835.869565
2016-01-19 1094 788.434783
2016-01-19 47512 25.739130
2016-01-19 29160 32.608696
2016-01-20 1746 565.173913
2016-01-20 1267 876.347826
2016-01-20 3107 390.652174
2016-01-20 1096 859.695652
2016-01-20 1319 749.478261
2016-01-20 1355 766.130435
2016-01-20 49850 24.826087
2016-01-20 36121 33.000000
2016-01-21 1828 555.695652
2016-01-21 1124 805.956522
2016-01-21 1923 401.782609
2016-01-21 2897 407.304348
2016-01-21 1220 848.739130
2016-01-21 1399 714.043478
2016-01-21 1582 854.217391
2016-01-21 1958 838.695652
2016-01-21 45371 27.652174
2016-01-21 34248 35.478261
2016-01-22 1232 514.913043
2016-01-22 727 821.478261
2016-01-22 1532 462.260870
2016-01-22 2090 431.043478
2016-01-22 842 768.130435
2016-01-22 1043 692.086957
2016-01-22 1284 892.043478
2016-01-22 1654 667.043478
2016-01-22 28863 29.000000
2016-01-22 21218 35.913043
2016-01-23 1167 502.173913
2016-01-23 798 847.739130
2016-01-23 1198 479.521739
2016-01-23 2135 433.043478
2016-01-23 775 769.173913
2016-01-23 856 679.826087
2016-01-23 1276 794.826087
2016-01-23 1192 629.391304
2016-01-24 1189 532.695652
2016-01-24 708 862.695652
2016-01-24 2028 474.652174
2016-01-24 1781 450.130435
2016-01-24 794 802.521739
2016-01-24 886 737.347826
2016-01-24 1120 750.782609
2016-01-24 1168 645.434783
2016-01-24 21527 36.782609
2016-01-24 18949 32.826087
2016-01-25 1250 539.304348
2016-01-25 779 894.130435
2016-01-25 1336 423.304348
2016-01-25 1703 501.217391
2016-01-25 1019 928.956522
2016-01-25 791 870.000000
2016-01-25 935 732.086957
2016-01-25 1087 811.565217
2016-01-25 1259 726.565217
2016-01-25 16278 46.739130
2016-01-25 20721 36.869565
2016-01-26 1368 545.782609
2016-01-26 924 902.130435
2016-01-26 1431 520.347826
2016-01-26 2316 507.782609
2016-01-26 949 932.826087
2016-01-26 931 843.956522
2016-01-26 1029 723.521739
2016-01-26 1204 702.347826
2016-01-26 16522 63.478261
2016-01-26 27454 33.217391
2016-01-27 1807 553.434783
2016-01-27 1317 874.086957
2016-01-27 2079 520.347826
2016-01-27 2487 482.565217
2016-01-27 1157 853.130435
2016-01-27 1213 757.086957
2016-01-27 1758 746.652174
2016-01-27 18981 77.739130
2016-01-27 34159 31.391304
2016-01-28 1639 554.608696
2016-01-28 1188 799.391304
2016-01-28 2361 514.521739
2016-01-28 2116 505.304348
2016-01-28 1147 840.000000
2016-01-28 1321 781.956522
2016-01-28 1935 726.347826
2016-01-28 17933 85.260870
2016-01-28 34537 30.826087
2016-01-29 1240 537.173913
2016-01-29 703 816.304348
2016-01-29 1480 456.391304
2016-01-29 1502 540.739130
2016-01-29 827 842.521739
2016-01-29 863 748.565217
2016-01-29 7055 164.739130
2016-01-29 386 736.652174
2016-01-29 13235 88.956522
2016-01-29 20230 30.913043
2016-01-30 1175 529.652174
2016-01-30 667 868.956522
2016-01-30 1409 467.521739
2016-01-30 1727 564.826087
2016-01-30 797 824.173913
2016-01-30 1197 789.565217
2016-01-30 7829 138.565217
2016-01-30 13021 87.304348
2016-01-30 16766 38.478261
2016-01-31 1149 532.739130
2016-01-31 711 896.478261
2016-01-31 1207 495.521739
2016-01-31 2059 475.086957
2016-01-31 790 781.086957
2016-01-31 739 676.043478
2016-01-31 6435 128.565217
2016-01-31 11656 91.043478
2016-01-31 15648 45.173913
2016-02-01 1270 547.086957
2016-02-01 773 884.434783
2016-02-01 1271 525.869565
2016-02-01 1649 473.521739
2016-02-01 928 777.652174
2016-02-01 751 772.565217
2016-02-01 6630 134.260870
2016-02-01 8485 105.260870
2016-02-01 16267 50.565217
2016-02-02 1435 549.434783
2016-02-02 1003 873.434783
2016-02-02 1181 588.391304
2016-02-02 1770 505.000000
2016-02-02 1080 747.652174
2016-02-02 4731 155.739130
2016-02-02 8677 145.565217
2016-02-02 18581 56.521739
2016-02-03 1858 563.478261
2016-02-03 1419 817.521739
2016-02-03 1409 630.000000
2016-02-03 2330 540.521739
2016-02-03 1361 768.043478
2016-02-03 2569 765.043478
2016-02-03 6081 197.130435
2016-02-03 9948 163.565217
2016-02-03 25123 57.173913
2016-02-04 1869 537.782609
2016-02-04 1312 813.956522
2016-02-04 1678 648.086957
2016-02-04 2164 550.304348
2016-02-04 1254 737.217391
2016-02-04 3304 621.826087
2016-02-04 4468 228.086957
2016-02-04 9212 183.956522
2016-02-04 23394 59.565217
2016-02-04 6235 262.086957
2016-02-05 1320 520.086957
2016-02-05 846 800.695652
2016-02-05 1293 616.869565
2016-02-05 1449 561.782609
2016-02-05 925 754.173913
2016-02-05 2616 541.304348
2016-02-05 2302 293.695652
2016-02-05 6666 186.130435
2016-02-05 14004 60.043478
2016-02-05 3760 248.391304
2016-02-06 1280 533.695652
2016-02-06 769 808.304348
2016-02-06 1014 612.695652
2016-02-06 1769 557.695652
2016-02-06 925 759.521739
2016-02-06 2343 530.391304
2016-02-06 1973 386.956522
2016-02-06 6154 177.565217
2016-02-06 13272 65.478261
2016-02-06 3408 295.260870
2016-02-06 3552 364.260870
2016-02-07 1439 536.434783
2016-02-07 929 850.434783
2016-02-07 979 654.217391
2016-02-07 1663 515.086957
2016-02-07 989 789.565217
2016-02-07 2189 521.739130
2016-02-07 1938 458.608696
2016-02-07 6039 181.217391
2016-02-07 12973 69.869565
2016-02-07 3955 334.391304
2016-02-07 3062 379.000000
2016-02-08 1359 530.869565
2016-02-08 815 838.304348
2016-02-08 1439 692.521739
2016-02-08 1667 554.956522
2016-02-08 927 768.913043
2016-02-08 2127 516.391304
2016-02-08 1618 515.260870
2016-02-08 5211 193.304348
2016-02-08 11992 78.173913
2016-02-08 1317 335.000000
2016-02-08 2002 435.304348
2016-02-09 1735 521.478261
2016-02-09 1020 816.739130
2016-02-09 1465 580.739130
2016-02-09 2006 607.391304
2016-02-09 1088 752.304348
2016-02-09 2027 548.434783
2016-02-09 1568 558.826087
2016-02-09 5879 210.043478
2016-02-09 15179 78.130435
2016-02-09 556 606.913043
2016-02-09 2652 537.695652
2016-02-10 2212 527.739130
2016-02-10 1385 764.434783
2016-02-10 1697 581.565217
2016-02-10 2673 575.869565
2016-02-10 1448 762.478261
2016-02-10 3024 627.956522
2016-02-10 1736 650.608696
2016-02-10 6547 229.260870
2016-02-10 20504 80.869565
2016-02-10 2061 569.391304
2016-02-11 2064 481.347826
2016-02-11 1238 785.565217
2016-02-11 1760 622.608696
2016-02-11 2236 552.000000
2016-02-11 1381 714.173913
2016-02-11 3513 602.695652
2016-02-11 1719 707.347826
2016-02-11 6110 261.956522
2016-02-11 17485 76.695652
2016-02-11 1916 794.652174
2016-02-12 1449 467.000000
2016-02-12 791 825.173913
2016-02-12 1235 604.130435
2016-02-12 1489 593.043478
2016-02-12 1001 709.000000
2016-02-12 2534 523.347826
2016-02-12 1232 723.000000
2016-02-12 4729 272.521739
2016-02-12 11147 79.086957
2016-02-13 1342 487.478261
2016-02-13 702 858.956522
2016-02-13 1728 563.434783
2016-02-13 1380 606.695652
2016-02-13 1009 678.956522
2016-02-13 2332 462.000000
2016-02-13 1271 739.304348
2016-02-13 4395 261.391304
2016-02-13 9598 82.260870
2016-02-14 2928 223.304348
2016-02-14 1280 506.347826
2016-02-14 674 904.260870
2016-02-14 949 473.565217
2016-02-14 1353 625.956522
2016-02-14 746 924.086957
2016-02-14 877 684.695652
2016-02-14 1954 451.130435
2016-02-14 826 742.739130
2016-02-14 1090 701.347826
2016-02-14 4079 261.043478
2016-02-14 10271 87.956522
2016-02-15 2029 278.826087
2016-02-15 1430 485.391304
2016-02-15 768 902.086957
2016-02-15 940 562.173913
2016-02-15 1441 606.608696
2016-02-15 969 692.000000
2016-02-15 2134 479.173913
2016-02-15 1316 726.478261
2016-02-15 4924 253.956522
2016-02-15 11795 78.000000
2016-02-16 1851 415.913043
2016-02-16 1582 480.956522
2016-02-16 1000 888.434783
2016-02-16 1409 644.608696
2016-02-16 1997 598.000000
2016-02-16 1079 726.913043
2016-02-16 1562 867.652174
2016-02-16 2062 503.173913
2016-02-16 1478 710.826087
2016-02-16 5353 251.782609
2016-02-16 15537 75.478261
2016-02-17 1723 561.956522
2016-02-17 1919 503.304348
2016-02-17 1307 865.000000
2016-02-17 1661 582.913043
2016-02-17 2217 562.391304
2016-02-17 1327 750.565217
2016-02-17 1960 807.521739
2016-02-17 2706 569.739130
2016-02-17 1865 747.043478
2016-02-17 8867 265.608696
2016-02-17 20417 74.652174
2016-02-18 1419 705.086957
2016-02-18 2025 506.782609
2016-02-18 1293 802.217391
2016-02-18 1821 584.217391
2016-02-18 2385 574.869565
2016-02-18 1310 771.304348
2016-02-18 1734 835.739130
2016-02-18 3556 552.391304
2016-02-18 2286 733.695652
2016-02-18 35279 184.565217
2016-02-18 20284 72.043478
2016-02-19 1357 798.869565
2016-02-19 1560 505.173913
2016-02-19 1021 810.000000
2016-02-19 715 628.000000
2016-02-19 1969 533.086957
2016-02-19 1170 775.478261
2016-02-19 1476 859.521739
2016-02-19 3099 496.043478
2016-02-19 1933 668.304348
2016-02-19 16330 78.826087
2016-02-19 19032 71.130435
2016-02-20 1302 513.260870
2016-02-20 856 844.695652
2016-02-20 1271 794.260870
2016-02-20 1555 542.043478
2016-02-20 946 780.913043
2016-02-20 1163 839.000000
2016-02-20 3295 443.869565
2016-02-20 1310 670.000000
2016-02-20 10265 98.652174
2016-02-20 14375 64.173913
2016-02-21 1357 508.869565
2016-02-21 799 826.521739
2016-02-21 1187 679.913043
2016-02-21 1413 560.130435
2016-02-21 972 771.695652
2016-02-21 1138 880.826087
2016-02-21 3777 381.652174
2016-02-21 1256 706.695652
2016-02-21 8114 119.260870
2016-02-21 14902 63.478261
2016-02-22 1396 508.086957
2016-02-22 789 821.217391
2016-02-22 1086 623.347826
2016-02-22 1486 600.391304
2016-02-22 1024 735.217391
2016-02-22 806 882.956522
2016-02-22 5447 321.782609
2016-02-22 820 657.739130
2016-02-22 1297 741.391304
2016-02-22 7352 150.478261
2016-02-22 18591 60.608696
2016-02-23 1654 499.739130
2016-02-23 1059 848.130435
2016-02-23 1381 589.217391
2016-02-23 1763 610.130435
2016-02-23 1149 717.173913
2016-02-23 3896 349.652174
2016-02-23 1469 736.043478
2016-02-23 7706 163.869565
2016-02-23 21589 45.000000
2016-02-24 2128 492.913043
2016-02-24 1373 777.217391
2016-02-24 1614 581.608696
2016-02-24 2165 589.608696
2016-02-24 1373 706.869565
2016-02-24 3034 431.347826
2016-02-24 2178 718.782609
2016-02-24 8076 187.347826
2016-02-24 29501 40.173913
2016-02-25 1936 477.739130
2016-02-25 1227 760.391304
2016-02-25 1717 638.000000
2016-02-25 983 933.434783
2016-02-25 1981 595.086957
2016-02-25 1319 734.652174
2016-02-25 3048 529.739130
2016-02-25 1689 693.478261
2016-02-25 5546 221.956522
2016-02-25 1647 619.304348
2016-02-25 31090 35.304348
2016-02-26 1480 468.173913
2016-02-26 811 774.130435
2016-02-26 1055 620.739130
2016-02-26 627 925.173913
2016-02-26 1183 612.086957
2016-02-26 962 725.130435
2016-02-26 2712 509.913043
2016-02-26 1020 762.695652
2016-02-26 4138 266.304348
2016-02-26 176 648.521739
2016-02-26 16154 34.521739
2016-02-27 1458 422.956522
2016-02-27 700 785.695652
2016-02-27 865 646.869565
2016-02-27 563 934.956522
2016-02-27 1314 668.826087
2016-02-27 892 696.913043
2016-02-27 2770 426.565217
2016-02-27 1199 781.130435
2016-02-27 4485 271.695652
2016-02-27 15148 41.130435
2016-02-28 1430 414.217391
2016-02-28 768 803.000000
2016-02-28 1364 670.347826
2016-02-28 1411 618.869565
2016-02-28 951 708.826087
2016-02-28 2600 374.608696
2016-02-28 1438 758.260870
2016-02-28 4460 260.695652
2016-02-28 15738 43.478261
2016-02-28 1047 880.260870
2016-03-01 1586 423.913043
2016-03-01 853 796.217391
2016-03-01 1590 560.869565
2016-03-01 662 957.086957
2016-03-01 3075 571.608696
2016-03-01 979 683.739130
2016-03-01 1548 398.913043
2016-03-01 1401 706.304348
2016-03-01 5233 245.565217
2016-03-01 19151 39.782609
2016-03-01 1200 745.434783
2016-03-02 2041 417.260870
2016-03-02 1012 813.565217
2016-03-02 1786 506.043478
2016-03-02 823 928.217391
2016-03-02 4436 431.434783
2016-03-02 1114 681.217391
2016-03-02 4691 477.260870
2016-03-02 1542 537.043478
2016-03-02 2221 635.086957
2016-03-02 5296 223.304348
2016-03-02 25378 34.826087
2016-03-02 1252 663.565217
2016-03-03 2322 393.304348
2016-03-03 1277 805.869565
2016-03-03 1508 524.391304
2016-03-03 1049 924.826087
2016-03-03 5262 371.695652
2016-03-03 1294 716.826087
2016-03-03 1317 872.565217
2016-03-03 6659 327.434783
2016-03-03 1706 683.217391
2016-03-03 2198 505.304348
2016-03-03 5575 253.652174
2016-03-03 33408 29.565217
2016-03-03 1982 662.434783
2016-03-04 2084 404.826087
2016-03-04 1123 801.304348
2016-03-04 1720 594.521739
2016-03-04 990 915.913043
2016-03-04 4766 364.130435
2016-03-04 1396 752.217391
2016-03-04 1115 904.434783
2016-03-04 6417 253.739130
2016-03-04 1925 776.739130
2016-03-04 1691 573.000000
2016-03-04 5401 291.782609
2016-03-04 32011 30.565217
2016-03-04 2568 612.521739
2016-03-05 1581 405.043478
2016-03-05 664 823.173913
2016-03-05 1888 566.000000
2016-03-05 631 892.000000
2016-03-05 3108 368.608696
2016-03-05 3348 572.695652
2016-03-05 1131 890.869565
2016-03-05 4743 255.695652
2016-03-05 1862 716.565217
2016-03-05 937 695.913043
2016-03-05 3862 293.869565
2016-03-05 32532 28.869565
2016-03-05 2426 496.521739
2016-03-06 1527 410.434783
2016-03-06 757 886.260870
2016-03-06 1526 437.260870
2016-03-06 786 832.913043
2016-03-06 3066 382.000000
2016-03-06 928 922.652174
2016-03-06 1710 350.260870
2016-03-06 1391 779.956522
2016-03-06 2689 246.260870
2016-03-06 1827 603.695652
2016-03-06 990 778.913043
2016-03-06 4908 289.695652
2016-03-06 33458 17.130435
2016-03-06 1779 412.652174
2016-03-07 1603 431.521739
2016-03-07 798 928.130435
2016-03-07 1207 496.043478
2016-03-07 1008 780.260870
2016-03-07 3151 398.304348
2016-03-07 1368 425.695652
2016-03-07 1529 659.130435
2016-03-07 1731 354.260870
2016-03-07 2077 595.000000
2016-03-07 1188 812.000000
2016-03-07 4436 257.869565
2016-03-07 34919 14.521739
2016-03-07 1640 447.478261
2016-03-08 1469 422.608696
2016-03-08 711 898.434783
2016-03-08 1096 612.956522
2016-03-08 757 702.347826
2016-03-08 2009 415.086957
2016-03-08 1082 553.043478
2016-03-08 1259 593.217391
2016-03-08 2015 562.565217
2016-03-08 2057 536.130435
2016-03-08 1143 768.130435
2016-03-08 3709 267.260870
2016-03-08 33293 13.652174
2016-03-08 1551 504.565217
2016-03-09 1639 436.086957
2016-03-09 957 930.086957
2016-03-09 1567 623.260870
2016-03-09 859 779.782609
2016-03-09 1851 507.869565
2016-03-09 1259 628.869565
2016-03-09 1520 613.260870
2016-03-09 2084 595.000000
2016-03-09 1915 497.652174
2016-03-09 1290 829.478261
2016-03-09 3816 303.826087
2016-03-09 35441 13.565217
2016-03-09 2183 473.173913
2016-03-10 1919 475.391304
2016-03-10 1280 902.000000
2016-03-10 1869 532.521739
2016-03-10 1209 801.130435
2016-03-10 2006 518.043478
2016-03-10 1477 645.000000
2016-03-10 998 877.217391
2016-03-10 1766 607.434783
2016-03-10 19010 135.608696
2016-03-10 2478 618.000000
2016-03-10 2616 571.478261
2016-03-10 1957 774.956522
2016-03-10 4539 333.391304
2016-03-10 41386 15.826087
2016-03-10 2946 408.347826
2016-03-12 1395 481.565217
2016-03-12 895 906.260870
2016-03-12 1154 575.956522
2016-03-12 919 752.565217
2016-03-12 1231 650.043478
2016-03-12 874 730.173913
2016-03-12 1602 657.086957
2016-03-12 16656 45.130435
2016-03-12 1771 557.260870
2016-03-12 2661 457.869565
2016-03-12 993 873.608696
2016-03-12 3257 365.086957
2016-03-12 28069 23.000000
2016-03-12 1447 404.913043
2016-03-13 1772 477.130435
2016-03-13 833 891.086957
2016-03-13 1127 615.434783
2016-03-13 1036 747.739130
2016-03-13 1188 691.826087
2016-03-13 898 764.565217
2016-03-13 2082 442.956522
2016-03-13 1502 599.130435
2016-03-13 4107 52.217391
2016-03-13 1385 595.260870
2016-03-13 2750 439.130435
2016-03-13 1027 931.608696
2016-03-13 2941 371.608696
2016-03-13 28991 22.826087
2016-03-13 872 531.130435
2016-03-14 1528 431.565217
2016-03-14 738 921.869565
2016-03-14 1084 654.652174
2016-03-14 871 783.043478
2016-03-14 1163 734.521739
2016-03-14 880 771.608696
2016-03-14 1433 507.608696
2016-03-14 1039 607.478261
2016-03-14 1761 116.565217
2016-03-14 1777 683.217391
2016-03-14 2917 411.739130
2016-03-14 909 891.173913
2016-03-14 3078 405.478261
2016-03-14 27801 20.782609
2016-03-14 815 710.913043
2016-03-15 1614 416.173913
2016-03-15 758 931.260870
2016-03-15 873 689.608696
2016-03-15 743 778.521739
2016-03-15 1139 751.695652
2016-03-15 953 764.782609
2016-03-15 1653 639.478261
2016-03-15 787 708.260870
2016-03-15 443 622.652174
2016-03-15 472 268.913043
2016-03-15 1107 634.000000
2016-03-15 2533 398.782609
2016-03-15 1234 912.608696
2016-03-15 3078 385.000000
2016-03-15 27124 18.869565
2016-03-15 674 854.347826
2016-03-16 1639 432.000000
2016-03-16 1088 981.000000
2016-03-16 1643 816.000000
2016-03-16 1036 822.000000
2016-03-16 1288 778.000000
2016-03-16 929 757.000000
2016-03-16 1993 632.000000
2016-03-16 779 824.000000
2016-03-16 151 826.000000
2016-03-16 48 463.000000
2016-03-16 913 725.000000
2016-03-16 2490 429.000000
2016-03-16 2112 807.000000
2016-03-16 3049 402.000000
2016-03-16 30309 20.000000
2016-03-16 704 957.000000
2016-03-16 23134 121.000000
2016-03-17 1826 471.521739
2016-03-17 1266 882.695652
2016-03-17 1674 632.869565
2016-03-17 1286 758.347826
2016-03-17 1518 778.347826
2016-03-17 1170 786.434783
2016-03-17 2531 593.826087
2016-03-17 1025 929.652174
2016-03-17 2808 477.695652
2016-03-17 1842 602.391304
2016-03-17 3495 426.434783
2016-03-17 34961 20.478261
2016-03-17 26072 70.565217
2016-03-18 1680 499.434783
2016-03-18 1114 894.434783
2016-03-18 1160 569.391304
2016-03-18 1154 719.695652
2016-03-18 1367 788.956522
2016-03-18 1143 811.913043
2016-03-18 2260 539.130435
2016-03-18 2897 522.086957
2016-03-18 1793 636.826087
2016-03-18 3170 468.000000
2016-03-18 32913 23.652174
2016-03-18 20929 49.913043
2016-03-19 1325 513.260870
2016-03-19 698 705.956522
2016-03-19 661 746.869565
2016-03-19 1070 786.956522
2016-03-19 902 790.000000
2016-03-19 1566 546.304348
2016-03-19 2300 477.608696
2016-03-19 1238 618.739130
2016-03-19 3016 451.391304
2016-03-19 24902 23.217391
2016-03-19 15211 55.956522
2016-03-20 1314 507.260870
2016-03-20 985 802.913043
2016-03-20 764 802.173913
2016-03-20 997 814.565217
2016-03-20 840 805.304348
2016-03-20 1505 538.521739
2016-03-20 2374 474.173913
2016-03-20 1297 646.521739
2016-03-20 1352 680.956522
2016-03-20 2679 407.739130
2016-03-20 29736 23.217391
2016-03-20 15297 57.391304
2016-03-21 1569 523.521739
2016-03-21 786 747.565217
2016-03-21 849 821.391304
2016-03-21 2474 823.260870
2016-03-21 973 830.086957
2016-03-21 1522 606.086957
2016-03-21 2116 493.695652
2016-03-21 1069 708.565217
2016-03-21 1135 715.739130
2016-03-21 2648 422.913043
2016-03-21 29122 17.043478
2016-03-21 15809 59.565217
2016-03-22 1392 487.869565
2016-03-22 1920 850.130435
2016-03-22 752 812.000000
2016-03-22 12717 676.130435
2016-03-22 896 773.695652
2016-03-22 1192 621.913043
2016-03-22 726 883.347826
2016-03-22 892 867.826087
2016-03-22 2114 528.086957
2016-03-22 885 785.565217
2016-03-22 934 775.521739
2016-03-22 2740 472.521739
2016-03-22 27754 18.913043
2016-03-22 6649 60.086957
2016-03-23 1514 486.130435
2016-03-23 1469 561.260870
2016-03-23 860 837.043478
2016-03-23 7450 260.652174
2016-03-23 923 771.304348
2016-03-23 794 783.565217
2016-03-23 1103 700.869565
2016-03-23 940 903.347826
2016-03-23 2628 501.260870
2016-03-23 1021 873.391304
2016-03-23 2653 490.347826
2016-03-23 31568 19.000000
2016-03-23 5017 107.695652
2016-03-24 1896 489.869565
2016-03-24 1529 561.956522
2016-03-24 1173 828.739130
2016-03-24 5589 160.217391
2016-03-24 1154 803.000000
2016-03-24 1359 762.956522
2016-03-24 1121 861.652174
2016-03-24 2612 473.391304
2016-03-24 1253 888.695652
2016-03-24 3124 471.000000
2016-03-24 35771 20.521739
2016-03-24 6328 181.391304
2016-03-25 1904 473.347826
2016-03-25 1733 611.086957
2016-03-25 1114 798.739130
2016-03-25 4362 229.260870
2016-03-25 1125 812.782609
2016-03-25 1204 797.608696
2016-03-25 1009 896.173913
2016-03-25 3617 485.086957
2016-03-25 1266 919.304348
2016-03-25 2958 496.478261
2016-03-25 35380 21.826087
2016-03-25 5478 211.782609
2016-03-26 1441 459.695652
2016-03-26 1508 559.478261
2016-03-26 708 794.565217
2016-03-26 2858 282.521739
2016-03-26 840 800.434783
2016-03-26 856 825.086957
2016-03-26 2804 946.826087
2016-03-26 2284 438.782609
2016-03-26 859 881.695652
2016-03-26 2744 503.478261
2016-03-26 28448 20.869565
2016-03-26 3938 234.173913
2016-03-27 1417 467.130435
2016-03-27 1104 515.521739
2016-03-27 740 846.000000
2016-03-27 2453 349.304348
2016-03-27 853 800.478261
2016-03-27 856 846.521739
2016-03-27 3839 412.391304
2016-03-27 2192 464.434783
2016-03-27 778 906.130435
2016-03-27 2786 487.391304
2016-03-27 30854 19.956522
2016-03-27 3834 248.913043
2016-03-28 1489 483.434783
2016-03-28 946 585.608696
2016-03-28 750 852.304348
2016-03-28 2510 406.826087
2016-03-28 914 795.347826
2016-03-28 847 891.434783
2016-03-28 4023 242.913043
2016-03-28 3930 424.260870
2016-03-28 3276 451.608696
2016-03-28 29605 17.652174
2016-03-28 3952 247.347826
2016-03-29 1457 493.347826
2016-03-29 1060 668.608696
2016-03-29 963 856.000000
2016-03-29 1390 449.217391
2016-03-29 998 788.086957
2016-03-29 924 931.260870
2016-03-29 1689 233.391304
2016-03-29 3328 315.000000
2016-03-29 3006 411.000000
2016-03-29 30412 16.826087
2016-03-29 3748 264.130435
2016-03-30 2236 494.391304
2016-03-30 1891 661.130435
2016-03-30 1497 736.043478
2016-03-30 1426 595.478261
2016-03-30 2628 707.956522
2016-03-30 1039 945.086957
2016-03-30 1117 431.086957
2016-03-30 1364 550.826087
2016-03-30 2926 337.086957
2016-03-30 1297 917.565217
2016-03-30 3257 455.391304
2016-03-30 3859 269.000000
2016-03-31 2041 445.000000
2016-03-31 1526 506.130435
2016-03-31 1157 622.652174
2016-03-31 1388 673.521739
2016-03-31 1800 500.913043
2016-03-31 2433 954.826087
2016-03-31 1022 905.434783
2016-03-31 1223 625.434783
2016-03-31 237 579.869565
2016-03-31 11099 389.565217
2016-03-31 1372 918.217391
2016-03-31 3294 479.130435
2016-03-31 35903 21.739130
2016-03-31 777 944.652174
2016-03-31 3770 312.956522
We will now extract the 80% values of this new dataframe and treat them as training data values.
train = newData2[:int(0.8*(len(newData2)))]import matplotlib.pyplot as pltNow let's plot the sales values so that it gives us an idea of how widely spread the sales values are. It gives us an insight on the range of values for sales.
newData2['sales'].plot()<matplotlib.axes._subplots.AxesSubplot at 0x15b73ee31d0>
In time series analysis and forecasting, it is important for us to know if the time series in stationary or not. This can be done by plotting a graph and more precisely by running some statistical tests.
There are many tests which can tell you about the stationarity of the time series data, like ADF test, KPSS test etc. We have used an ADF (Dickey-Fuller) test to determine this.
from statsmodels.tsa.stattools import adfuller
def adf_test(timeseries):
#Perform Dickey-Fuller test:
print ('Results of Dickey-Fuller Test:')
dftest = adfuller(timeseries, autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,value in dftest[4].items():
dfoutput['Critical Value (%s)'%key] = value
print (dfoutput)
#apply adf test on the series
adf_test(train['sales'])Results of Dickey-Fuller Test:
Test Statistic -4.060158
p-value 0.001126
#Lags Used 18.000000
Number of Observations Used 716.000000
Critical Value (1%) -3.439516
Critical Value (5%) -2.865585
Critical Value (10%) -2.568924
dtype: float64
In this test, as the Test Statistic is less than the critical value, we can reject the null hypothesis, which also means that the series is stationary. If the test statistic was higher than the critical value, then we cannot reject the null hypothesis and the time series in that case will be non-stationary. Differencing is required to convert a non stationary time series into a stationary one.
It is important for us to understand which model to be used with respect to the problem statement. In our case, we should notice that the time series is not a univariate time series but a multivariate time series. In such time series, there are more than one variables to be fed in the model as input. As we know here, Sales is a function of Time and Ranks, so this problem cannot be solved using ARIMA model. Instead, we will use a Vector Autoregression Model and train it to understand that Sales is a function of Time and Rank.
from statsmodels.tsa.vector_ar.var_model import VAR
model = VAR(endog=train)
results = model.fit()C:\Users\dhira\Anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
' ignored when e.g. forecasting.', ValueWarning)
results.summary() Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 21, Nov, 2018
Time: 05:58:08
--------------------------------------------------------------------
No. of Equations: 2.00000 BIC: 28.9941
Nobs: 734.000 HQIC: 28.9710
Log likelihood: -12704.0 FPE: 3.76394e+12
AIC: 28.9565 Det(Omega_mle): 3.73336e+12
--------------------------------------------------------------------
Results for equation sales
=============================================================================
coefficient std. error t-stat prob
-----------------------------------------------------------------------------
const 7188.095553 1327.119449 5.416 0.000
L1.sales -0.013136 0.050402 -0.261 0.794
L1.avgrank -3.750460 1.978577 -1.896 0.058
=============================================================================
Results for equation avgrank
=============================================================================
coefficient std. error t-stat prob
-----------------------------------------------------------------------------
const 499.568193 33.416604 14.950 0.000
L1.sales -0.002050 0.001269 -1.616 0.106
L1.avgrank 0.107571 0.049820 2.159 0.031
=============================================================================
Correlation matrix of residuals
sales avgrank
sales 1.000000 -0.681426
avgrank -0.681426 1.000000
Lets have a look at the variance of the sales and ranks data. These plots are generated from the interpretation of the training data by VAR model.
results.plot()
Lets plot the auto-correlation function to understand the correlation for time series observations with respect to the previous time steps, called lags.
results.plot_acorr()
As we can see in the plot, the values of y-axis are the confidence intervals and the values on x-axis are the lags. The two horizontal dotted lines in a plot determines a range of the time-series values w.r.t the lags. This plot also gives us the information of the number of lags that we can use in our VAR model. In our case, we can use maxlags = 15 .
results = model.fit(maxlags=15, ic='aic')lag_order = results.k_arLet's fit the model on entire data set and forecast 90 days ahead of current time. We are given 3 months of data and we will predict the sales and ranks for 3 months in future.
Estimates = results.forecast(newData2.values[-lag_order:], 90)
print(Estimates)[[ 2840.9997398 766.55819819]
[-3328.33224535 768.92426941]
[ 3442.98895128 711.5326563 ]
[ 6847.15397636 608.70795631]
[11409.9897393 483.837487 ]
[13394.93730923 464.58679922]
[ 4279.04575772 540.87150138]
[ 8296.26502653 464.07885361]
[ 4054.8054321 605.09264174]
[ 231.85003493 696.99709723]
[11334.16608751 465.13579213]
[ 5403.44923458 672.80952569]
[ 5432.91721041 586.72654792]
[ 8019.8436417 592.6446544 ]
[ 4546.70601552 567.03138658]
[ 6033.86097957 565.91417052]
[ 5403.18085084 553.77807264]
[ 6431.92055022 525.28791605]
[ 8427.96098473 522.71537805]
[ 5305.53989777 567.49954485]
[ 6536.30019512 570.64005632]
[ 6193.72228193 573.12941588]
[ 3804.96923387 633.59549812]
[ 6048.63990018 570.52243336]
[ 5762.96746691 575.97963205]
[ 5780.71257099 556.68051503]
[ 6849.01283309 542.48619231]
[ 5855.8400033 537.36308196]
[ 6305.73027813 544.80184995]
[ 5722.99811567 558.13645455]
[ 5368.1139812 568.10714192]
[ 5982.92748944 571.36146014]
[ 5302.00933452 577.14655284]
[ 5565.58875399 579.0085594 ]
[ 5889.43201279 558.93154261]
[ 5391.16869031 566.01017793]
[ 5725.38322837 553.05561002]
[ 5787.20073179 547.37273024]
[ 5683.40167542 549.25198014]
[ 5871.70661985 551.5172317 ]
[ 5543.95618446 557.28544658]
[ 5605.45301218 562.77021548]
[ 5453.57236052 566.47280035]
[ 5277.64111132 567.98414763]
[ 5455.24004668 564.81428074]
[ 5420.77687744 558.34029579]
[ 5483.37308721 556.92665379]
[ 5614.47621425 549.23832338]
[ 5548.71973182 550.08416308]
[ 5521.40008802 552.08441466]
[ 5470.25830851 553.7995956 ]
[ 5368.14962473 558.08819024]
[ 5364.01624925 560.07640496]
[ 5289.35440125 560.96506812]
[ 5315.15708607 560.0126222 ]
[ 5335.99469679 557.35228042]
[ 5329.4711038 554.75872575]
[ 5370.52402674 552.83070783]
[ 5375.2815719 550.53957805]
[ 5355.99243486 551.79089462]
[ 5337.64801989 552.34993771]
[ 5302.80672908 553.99701946]
[ 5258.75902116 556.08533622]
[ 5231.83059313 556.54143467]
[ 5210.17096186 556.70056974]
[ 5217.37835417 555.51318282]
[ 5223.60186681 553.90861148]
[ 5237.95884152 552.43010842]
[ 5248.17030834 551.25247297]
[ 5242.54236078 550.86511299]
[ 5225.50938114 551.538643 ]
[ 5203.92100952 552.11356579]
[ 5178.01533968 553.28987422]
[ 5153.64614402 553.94213406]
[ 5144.24416857 553.92870515]
[ 5137.4719728 553.6347964 ]
[ 5139.95256492 552.68124278]
[ 5144.52145518 551.79885458]
[ 5146.85829413 551.00722072]
[ 5145.82042273 550.58393884]
[ 5137.23587609 550.64909281]
[ 5123.84952527 551.01838741]
[ 5109.50839633 551.47860867]
[ 5092.83408089 551.98765892]
[ 5081.35985583 552.10870262]
[ 5075.40449536 551.97897494]
[ 5071.2599925 551.59387601]
[ 5073.11083862 550.99538795]
[ 5073.39811032 550.50581757]
[ 5072.09376233 550.12526407]]
Lets plot the forecasted values against the Observed value and Std. error.
results.plot_forecast(15)
We have set our start date to 2016-04-01 and forecasted the values for three months for two variables i.e., ranks and sales. We also appended the app id colum from the previous dataframes and the final data looks like below.
dataset = pd.DataFrame({'sales_est':Estimates[:,0],'ranks_est':Estimates[:,1]})
date = np.array('2016-04-01', dtype=np.datetime64)
Futuredate = date + np.arange(90)
df = pd.DataFrame(data =Futuredate,columns=['Dates'])
df['app_id'] = salesdata['app_id'].astype(int)
df['sales_est'] = dataset['sales_est'].astype(int)
df['ranks_est'] = dataset['ranks_est'].astype(int)
print(df)
Dates app_id sales_est ranks_est
0 2016-04-01 320 2840 766
1 2016-04-02 406 -3328 768
2 2016-04-03 459 3442 711
3 2016-04-04 722 6847 608
4 2016-04-05 1234 11409 483
5 2016-04-06 1490 13394 464
6 2016-04-07 2398 4279 540
7 2016-04-08 2891 8296 464
8 2016-04-09 320 4054 605
9 2016-04-10 406 231 696
.. ... ... ... ...
80 2016-06-20 2891 5137 550
81 2016-06-21 320 5123 551
82 2016-06-22 406 5109 551
83 2016-06-23 459 5092 551
84 2016-06-24 722 5081 552
85 2016-06-25 907 5075 551
86 2016-06-26 1234 5071 551
87 2016-06-27 1490 5073 550
88 2016-06-28 2346 5073 550
89 2016-06-29 2398 5072 550
[90 rows x 4 columns]
As required in the problem statement, we are required to save the data in csv format containing the above columns. to_csv function will save the dataframe in the correct working directory.
df.to_dense().to_csv("estimates.csv", index = False, sep=',', encoding='utf-8')Let's find the top 10 app_id by sales by running a simple command as below:
df1 = df.nlargest(10, 'sales_est', keep='first')
print(df1) Dates app_id sales_est ranks_est
5 2016-04-06 1490 13394 464
4 2016-04-05 1234 11409 483
10 2016-04-11 459 11334 465
18 2016-04-19 406 8427 522
7 2016-04-08 2891 8296 464
13 2016-04-14 1490 8019 592
26 2016-04-27 406 6849 542
3 2016-04-04 722 6847 608
20 2016-04-21 722 6536 570
17 2016-04-18 320 6431 525
We can visualize the app_id by total sales and take a quick look as to which app_id exhibited the most sales as per our forecast.
df1.plot(x = 'app_id' , y = 'sales_est',kind = 'bar')<matplotlib.axes._subplots.AxesSubplot at 0x15b0046c8d0>
We can also visualize top 10 ranks in terms of total sales.
df1.plot(x = 'ranks_est' , y = 'sales_est',kind = 'barh')<matplotlib.axes._subplots.AxesSubplot at 0x15b0069e2b0>
The Main agenda was to find out an estimation of sales as a factor of ranks and see if there is any correlation between these two properties. We can surely derive that there is a correalation between these two as depicted in the graphs as well. SOme insights worth noting are as below:
- The top 5 app_id by sales are 1490,1234,459,406, & 2891 as per our forecast model. This is also in alignment with the observed values from the given datasets.
- There is surely an influence of higher ranks on higher sales. But, this cannot be drawn as an inference that the highest ranks will have the highest sales. Because, the app_id with the rank 483 has exhibited higher sales than 465 and so is the case with rank 570 and 525.
Our script is only taking avg rank as an affecting factor on Sales as of now. If given more time on this, I think a possible improvement can be to feed the hourly rank data in our Model instead of avg rank so that we can bank upon the accuracy of the model. Also, for the sake of time complexity, we considered only two independent variables Date, Ranks but we can definitely consider ratings and other factors while forecasting the Sales values while expediting on the time complexity of the model too.
## Thank you ##