drop rows with nan in specific 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: dropping nan in pandas dataframe

df.dropna(subset=['name', 'born'])

Example 4: 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 5: drop columns with nan pandas

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Example 6: pandas dropna

df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

##Drop the rows where at least one element is missing.
>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25