drop all rows with nan in a column pandas code example

Example 1: drop if nan in column pandas

df = df[df['EPS'].notna()]

Example 2: remove rows or columns with NaN value

df.dropna()     #drop all rows that have any NaN values
df.dropna(how='all')

Example 3: pandas drop row with nan

import pandas as pd

df = pd.DataFrame({'values_1': ['700','ABC','500','XYZ','1200'],
                   'values_2': ['DDD','150','350','400','5000'] 
                   })

df = df.apply (pd.to_numeric, errors='coerce')
df = df.dropna()
df = df.reset_index(drop=True)

print (df)

Example 4: pandas drop rows with nan in a particular column

In [30]: df.dropna(subset=[1])   #Drop only if NaN in specific column (as asked in the question)
Out[30]:
          0         1         2
1  2.677677 -1.466923 -0.750366
2       NaN  0.798002 -0.906038
3  0.672201  0.964789       NaN
5 -1.250970  0.030561 -2.678622
6       NaN  1.036043       NaN
7  0.049896 -0.308003  0.823295
9 -0.310130  0.078891       NaN

Example 5: remove all rows where one ccolumns egale to nan

#remove in dataframe but no in the file
df = df[df['column'].notna()]

#remove in dataframe and in the file
df.dropna(subset=['EPS'], how='all', inplace=True)

Example 6: remove a rows in which three column has nan

df.dropna(subset=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'], how='all', inplace=True)