drop columns with all nan 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 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: Returns a new DataFrame omitting rows with null values

# Returns a new DataFrame omitting rows with null values

df4.na.drop().show()
# +---+------+-----+
# |age|height| name|
# +---+------+-----+
# | 10|    80|Alice|
# +---+------+-----+

Example 6: threshold meaning in 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.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT