pandas

问题 df=pd.read_excel(filename) What attributes should I add to exclude header and footer? 回答1: Head over here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html you will find pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser

问题 df=pd.read_excel(filename) What attributes should I add to exclude header and footer? 回答1: Head over here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html you will find pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser

问题 I have the below data. I need to fill in the data for the remaining months I need only the first day [day one] of the month to be filled in. Wherever there is no data, I need the value to be filled with '0'. For example below is the existing data uname month_first msg_count 0 ArtCort0324 2017-06-01 9 I need output in below way. 回答1: Create a multiindex from combination of unman and date range and reindex the data df.month_first = pd.to_datetime(df.month_first) dates = pd.date_range(datetime

问题 I have a data frame as shown below B_ID Session no_show cumulative_no_show 1 s1 0.4 0.4 2 s1 0.6 1.0 3 s1 0.2 1.2 4 s1 0.1 1.3 5 s1 0.4 1.7 6 s1 0.2 1.9 7 s1 0.3 2.2 10 s2 0.3 0.3 11 s2 0.4 0.7 12 s2 0.3 1.0 13 s2 0.6 1.6 14 s2 0.2 1.8 15 s2 0.5 2.3 where cumulative_no_show is the cumulative sum of no_show. From the above I would like to create a new column called u_no_show based on below condition. Whenever cumulative_no_show >= 0.8, then subtract 1 from next cumulative_no_show. and so on.

问题 I have a data frame as shown below B_ID Session no_show cumulative_no_show 1 s1 0.4 0.4 2 s1 0.6 1.0 3 s1 0.2 1.2 4 s1 0.1 1.3 5 s1 0.4 1.7 6 s1 0.2 1.9 7 s1 0.3 2.2 10 s2 0.3 0.3 11 s2 0.4 0.7 12 s2 0.3 1.0 13 s2 0.6 1.6 14 s2 0.2 1.8 15 s2 0.5 2.3 where cumulative_no_show is the cumulative sum of no_show. From the above I would like to create a new column called u_no_show based on below condition. Whenever cumulative_no_show >= 0.8, then subtract 1 from next cumulative_no_show. and so on.

问题 I've been struggling to convert two columns in a pandas. The frame contains many columns, and 2 columns with dates: 'datelog'(is a date) and 'Timeofday' (is a time). The column datelog is a string. The column Timeofday is a datetime.time() format. The dateframe display is as folows: datelog Timeofday 0 30-APR-15 14:15:43 1 30-APR-15 14:16:13 2 30-APR-15 14:16:43 3 30-APR-15 14:17:13 4 30-APR-15 14:17:43 5 30-APR-15 14:18:13 6 30-APR-15 14:18:43 7 30-APR-15 14:19:13 8 30-APR-15 14:19:43 9 30

问题 I've been struggling to convert two columns in a pandas. The frame contains many columns, and 2 columns with dates: 'datelog'(is a date) and 'Timeofday' (is a time). The column datelog is a string. The column Timeofday is a datetime.time() format. The dateframe display is as folows: datelog Timeofday 0 30-APR-15 14:15:43 1 30-APR-15 14:16:13 2 30-APR-15 14:16:43 3 30-APR-15 14:17:13 4 30-APR-15 14:17:43 5 30-APR-15 14:18:13 6 30-APR-15 14:18:43 7 30-APR-15 14:19:13 8 30-APR-15 14:19:43 9 30

问题 I have data that looks like this: user in out location flag Time 0 ron 12/21/2021 10:11 12/21/2016 17:50 home 0 4:19:03 1 ron 12/21/2016 13:26 12/21/2016 13:52 office 2 0:25:28 2 april 12/21/2016 8:12 12/21/2016 17:27 office 0 8:15:03 3 april 12/21/2016 18:54 12/21/2016 22:56 office 0 4:02:36 4 andy 12/21/2016 8:57 12/21/2016 12:15 home 0 2:59:40 I want to sum or take the max value of time per user based on the flag. So I converted the column to timedelta. sample.loc[:,'Time'] = pd.to

问题 I have data that looks like this: user in out location flag Time 0 ron 12/21/2021 10:11 12/21/2016 17:50 home 0 4:19:03 1 ron 12/21/2016 13:26 12/21/2016 13:52 office 2 0:25:28 2 april 12/21/2016 8:12 12/21/2016 17:27 office 0 8:15:03 3 april 12/21/2016 18:54 12/21/2016 22:56 office 0 4:02:36 4 andy 12/21/2016 8:57 12/21/2016 12:15 home 0 2:59:40 I want to sum or take the max value of time per user based on the flag. So I converted the column to timedelta. sample.loc[:,'Time'] = pd.to

问题 I have data that looks like this: user in out location flag Time 0 ron 12/21/2021 10:11 12/21/2016 17:50 home 0 4:19:03 1 ron 12/21/2016 13:26 12/21/2016 13:52 office 2 0:25:28 2 april 12/21/2016 8:12 12/21/2016 17:27 office 0 8:15:03 3 april 12/21/2016 18:54 12/21/2016 22:56 office 0 4:02:36 4 andy 12/21/2016 8:57 12/21/2016 12:15 home 0 2:59:40 I want to sum or take the max value of time per user based on the flag. So I converted the column to timedelta. sample.loc[:,'Time'] = pd.to