PYTHON

drop rows with certain values pandas

#to drop rows based on certain condition in a column
import pandas as pd

df = df[df['column']!=1]

pandas drop rows with value in list

import pandas as pd

a = ['2015-01-01' , '2015-02-01']

df = pd.DataFrame(data={'date':['2015-01-01' , '2015-02-01', '2015-03-01' , '2015-04-01', '2015-05-01' , '2015-06-01']})

print(df)
#         date
#0  2015-01-01
#1  2015-02-01
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

df = df[~df['date'].isin(a)]

print(df)
#         date
#2  2015-03-01
#3  2015-04-01
#4  2015-05-01
#5  2015-06-01

remove particular row number in pandas

data = data.drop(data.index[1])

drop rows from dataframe based on column value

def filter_rows_by_values(df, col, values):
    return df[~df[col].isin(values)]

pandas drop row from a list of value

df = df[~df.datecolumn.isin(a)]

removing rows with specific column values from a dataframe

+---+--------------------------+----------------------------------+-----------+
| 1 | Sign up date | no_stores | no_unin_app     no_stores_recei  | ed_order  |
+---+--------------------------+----------------------------------+-----------+
| 2 | 2020-04-01   |      1    |             0                    |   0       |
| 3 | 2020-04-04   |     11    |             3                    |   6       |
| 4 | 2020-04-13   |      8    |             1                    |   4       |
+---+--------------------------+----------------------------------+-----------+

dataframe drop all rows with value

# create a new dataframe CONTAINING ONLY the rows, where the value
# of some_column = some_value
new_df = old_df.loc[old_df['some_column']=='some_value']
# create a new dataframe EXCLUDING the rows, where the value of
# some_column = some_value
new_df = old_df.loc[old_df['some_column']!='some_value']