drop if nan in column pandas
df = df[df['EPS'].notna()]remove rows or columns with NaN value
df.dropna() #drop all rows that have any NaN values
df.dropna(how='all')drop_na in pandas
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
# making new data frame with dropped NA values
new_data = data.dropna(axis = 0, how ='any')
drop na in pandas
df.dropna(inplace = True)
or
df_new = df.dropna()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|
# +---+------+-----+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 1940-04-25Copyright © 2021 Codeinu
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