I've taken a large data file and managed to use groupby and value_counts to get the dataframe below. However, I want to format it so the company is on the left, with the months on top, and each number would be the number of calls that month, the third column.
Here is my code to sort:
data = pd.DataFrame.from_csv('MYDATA.csv')
data[['recvd_dttm','CompanyName']]
data['recvd_dttm'].value_counts()
count = data.groupby(["recvd_dttm","CompanyName"]).size()
df = pd.DataFrame(count)
df.pivot(index='recvd_dttm', columns='CompanyName', values='NumberCalls')
Here is my output df=
recvd_dttm CompanyName
1/1/2015 11:42 Company 1 1
1/1/2015 14:29 Company 2 1
1/1/2015 8:12 Company 4 1
1/1/2015 9:53 Company 1 1
1/10/2015 11:38 Company 3 1
1/10/2015 11:31 Company 5 1
1/10/2015 12:04 Company 2 1
I want
Company Jan Feb Mar Apr May
Company 1 10 4 45 40 34
Company 2 2 5 56 5 57
Company 3 3 7 71 6 53
Company 4 4 4 38 32 2
Company 5 20 3 3 3 29
I know that there is a nifty pivot function for dataframes from this documentation http://pandas.pydata.org/pandas-docs/stable/reshaping.html for pandas, so I've been trying to use df.pivot(index='recvd_dttm', columns='CompanyName', values='NumberCalls')
One problem is that the third column doesn't have a name, so I can't use it for values = 'NumberCalls'. The second problem is figuring out how to take the datetime format in my dataframe and make it display by month only.
Edit:
CompanyName is the first column, recvd_dttm is the 15th column. This is my code after some more attempts:
data = pd.DataFrame.from_csv('MYDATA.csv')
data[['recvd_dttm','CompanyName']]
data['recvd_dttm'].value_counts()
RatedCustomerCallers = data['CompanyName'].value_counts()
count = data.groupby(["recvd_dttm","CompanyName"]).size()
df = pd.DataFrame(count).set_index('recvd_dttm').sort_index()
df.index = pd.to_datetime(df.index, format='%m/%d/%Y %H:%M')
result = df.groupby([lambda idx: idx.month, 'CompanyName']).agg({df.columns[1]: sum}).reset_index()
result.columns = ['Month', 'CompanyName', 'NumberCalls']
result.pivot(index='recvd_dttm', columns='CompanyName', values='NumberCalls')
It is throwing this error: KeyError: 'recvd_dttm' and won't get to the result line.
You need to aggregate the data before creating the pivot table. If there is no column name, you can either refer it to df.iloc[:, 1] (the 2nd column) or simply rename the df.
import pandas as pd
import numpy as np
# just simulate your data
np.random.seed(0)
dates = np.random.choice(pd.date_range('2015-01-01 00:00:00', '2015-06-30 00:00:00', freq='1h'), 10000)
company = np.random.choice(['company' + x for x in '1 2 3 4 5'.split()], 10000)
df = pd.DataFrame(dict(recvd_dttm=dates, CompanyName=company)).set_index('recvd_dttm').sort_index()
df['C'] = 1
df.columns = ['CompanyName', '']
Out[34]:
CompnayName
recvd_dttm
2015-01-01 00:00:00 company2 1
2015-01-01 00:00:00 company2 1
2015-01-01 00:00:00 company1 1
2015-01-01 00:00:00 company2 1
2015-01-01 01:00:00 company4 1
2015-01-01 01:00:00 company2 1
2015-01-01 01:00:00 company5 1
2015-01-01 03:00:00 company3 1
2015-01-01 03:00:00 company2 1
2015-01-01 03:00:00 company3 1
2015-01-01 04:00:00 company4 1
2015-01-01 04:00:00 company1 1
2015-01-01 04:00:00 company3 1
2015-01-01 05:00:00 company2 1
2015-01-01 06:00:00 company5 1
... ... ..
2015-06-29 19:00:00 company2 1
2015-06-29 19:00:00 company2 1
2015-06-29 19:00:00 company3 1
2015-06-29 19:00:00 company3 1
2015-06-29 19:00:00 company5 1
2015-06-29 19:00:00 company5 1
2015-06-29 20:00:00 company1 1
2015-06-29 20:00:00 company4 1
2015-06-29 22:00:00 company1 1
2015-06-29 22:00:00 company2 1
2015-06-29 22:00:00 company4 1
2015-06-30 00:00:00 company1 1
2015-06-30 00:00:00 company2 1
2015-06-30 00:00:00 company1 1
2015-06-30 00:00:00 company4 1
[10000 rows x 2 columns]
# first groupby month and company name, and calculate the sum of calls, and reset all index
# since we don't have a name for that columns, simply tell pandas it is the 2nd column we try to count on
result = df.groupby([lambda idx: idx.month, 'CompanyName']).agg({df.columns[1]: sum}).reset_index()
# rename the columns
result.columns = ['Month', 'CompanyName', 'counts']
Out[41]:
Month CompanyName counts
0 1 company1 328
1 1 company2 337
2 1 company3 342
3 1 company4 345
4 1 company5 331
5 2 company1 295
6 2 company2 300
7 2 company3 328
8 2 company4 304
9 2 company5 329
10 3 company1 366
11 3 company2 398
12 3 company3 339
13 3 company4 336
14 3 company5 345
15 4 company1 322
16 4 company2 348
17 4 company3 351
18 4 company4 340
19 4 company5 312
20 5 company1 347
21 5 company2 354
22 5 company3 347
23 5 company4 363
24 5 company5 312
25 6 company1 316
26 6 company2 311
27 6 company3 331
28 6 company4 307
29 6 company5 316
# create pivot table
result.pivot(index='CompanyName', columns='Month', values='counts')
Out[44]:
Month 1 2 3 4 5 6
CompanyName
company1 326 297 339 337 344 308
company2 310 318 342 328 355 296
company3 347 315 350 343 347 329
company4 339 314 367 353 343 311
company5 370 331 370 320 357 294
