How to Drop a List of Rows from Pandas Dataframe

How to drop a list of rows from Pandas dataframe?

Use DataFrame.drop and pass it a Series of index labels:

In [65]: df
Out[65]: 
       one  two
one      1    4
two      2    3
three    3    2
four     4    1

In [66]: df.drop(df.index[[1,3]])
Out[66]: 
       one  two
one      1    4
three    3    2

Drop a list of rows from Pandas df

For dropping rows:

df.drop(rows,inplace=True)

For dropping columns:

df.drop(cols,axis=1,inplace=True)

Test:

df = pd.DataFrame({'A':[1,2,3,4,5],'B':[1,2,3,4,5]})
print('DataFrame with initialised :\n',df,'\n')
df.drop([2,4],inplace=True)
print('DataFrame after dropping 2 rows:\n',df,'\n')
df.drop(['B'],axis=1,inplace=True)
print('DataFrame after dropping 1 column:\n',df,'\n')

Output:

DataFrame with initialised :
   A  B
0  1  1
1  2  2
2  3  3
3  4  4
4  5  5 

DataFrame after dropping 2 rows:
   A  B
0  1  1
1  2  2
3  4  4 

DataFrame after dropping 1 column:
   A
0  1
1  2
3  4 

dropping rows from dataframe based on a not in condition

You can use pandas.Dataframe.isin.

pandas.Dateframe.isin will return boolean values depending on whether each element is inside the list a or not. You then invert this with the ~ to convert True to False and vice versa.

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

How to drop rows from a pandas dataframe based on a pre-made list

You can do this using pandas.Dataframe.isin. This will return boolean values checking whether each element is inside the list x. You can then use the boolean values and take out the subset of the df with rows that return True by doing df[df['City'].isin(x)]. Following is my solution:

import pandas as pd

x = ['Paris' , 'Marseille']
df = pd.DataFrame(data={'City':['Paris', 'London', 'New York', 'Marseille'],
                        'Age':[1, 2, 3, 4]})

print(df)

df = df[df['City'].isin(x)]
print(df)

Output:

>>>         City  Age
0      Paris    1
1     London    2
2   New York    3
3  Marseille    4
        City  Age
0      Paris    1
3  Marseille    4

Pandas: How to remove rows from a dataframe based on a list?

You need

new_df = sales[~sales.CustomerID.isin(badcu)]

PANDAS drop a range of rows from df

Use slice to select the part you want:

df[:-m]

If you want to remove some middle rows, you can use drop:

df.drop(df.index[3:5])

Drop rows by index from dataframe

Change it to

df_train.drop(wrong_indexes_train,axis=1)

Dropping list of rows from multi-level pandas dataframe without a loop

Create the MultiIndex index then drop it

dfmi.drop(pd.MultiIndex.from_arrays([As,Bs]))

Droping a range of rows in pandas

The best way to do this is by indexing: df.drop(df.index[0:9]). This example will drop the first ten rows.

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