1 import numpy as np
   2 import pandas as pd

   1 path = '/path/to/file(s)'
   2 df = pd.read_csv(path + 'name.csv', sep=';', header=0, dtype={'Force String Column': str}, 
   3                  parse_dates=['Date Column'] encoding='utf-8')

   1 dt.rename(columns={'File column name 1': 'Column1', 
   2                    'File column name 2': '2'
   3                   }, inplace=True)

   1 df.drop('Column A', axis=1, inplace=True)

   1 df.loc[:, ['X']] = df.loc[:, ['X']].apply(lambda x: x.Y)

   1 df = pd.concat([df1, df2])
   2 df.reset_index(drop=True, inplace=True)

   1 df = df[df['A']str.match('regex')]

   1 df = pd.merge(df1, df2, on='Column A', how='left', suffixes=('_1','_2'))
   2 df = df.fillna(0)                    # To replace NaN fields with 0

   1 df = df.sort_values('Column A', ascending = False)

   1 pvt_df = pd.pivot_table(df, 
   2                      values=['Column B', 'Column C'], 
   3                      index='Column A', 
   4                      aggfunc={'Column B': [np.sum, np.average],
   5                               'Column C' : lambda x: len(x)})
   6 pvt_df .reset_index(inplace=True)         # Make Column A a column again, instead of index
   7 pvt_df .columns = [' '.join(col).strip() for col in 
   8                    pvt_df.columns.values] # Flatten multi-index columns
   9 pvt_df .rename(columns={                  # Rename known numbers
  10                     'Column B average':'average_B',
  11                     'Column B sum':'sum_B',
  12                     'Column C <lambda>':'count_C'
  13                    }, inplace=True)

   1 mask = df.loc[:,'Column X'].isnull()
   2 df.loc[mask,['Column B', 'Column C']] = df.loc[mask,['Column A', 'Column B']].values