drop if nan in column pandas
df = df[df['EPS'].notna()]drop null rows pandas
df.dropna()pandas remove rows with null in column
df = df[df['EPS'].notna()]clean nas from column pandas
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25Returns 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|
# +---+------+-----+pandas remove blank rows
In [27]: df
Out[27]:
A B C
0 -0.120211 -0.540679 -0.680481
1 NaN -2.027325 1.533582
2 NaN NaN 0.461821
3 -0.788073 NaN NaN
4 -0.916080 -0.612343 NaN
5 -0.887858 1.033826 NaN
6 1.948430 1.025011 -2.982224
7 0.019698 -0.795876 -0.046431
#The "subset" argument is used to select columns for which you want the NaN values to be dropped
#Set "inplace=True" if you want the new table to overwrite the old table.
In [28]: df.dropna(subset=['A', 'B'], inplace=True)
Out[28]:
A B C
0 -0.120211 -0.540679 -0.680481
1 -0.916080 -0.612343 NaN
2 -0.887858 1.033826 NaN
3 1.948430 1.025011 -2.982224
4 0.019698 -0.795876 -0.046431Copyright © 2021 Codeinu
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