python Missing value processing fillna mean value _Python Processing missing values


First create a with missing values NaN(Not A Number) of CSV(comma-separated values) file ：

import pandas as pdfrom io import StringIOcsv_data =
"""A,B,C,D1.0,2.0,3.0,4.05.0,6.0,,8.00.0,11.0,12.0,"""# If you are using Python
2.7, you need# to convert the string to unicode:# csv_data =
unicode(csv_data)df = pd.read_csv(StringIO(csv_data))1

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Delete samples with missing values

Specific treatment method ：

df.isnull()# Is the missing value returned True, Otherwise range False

df.isnull().sum()# Returns the number of missing values contained in each column

df.dropna()# Directly delete rows with missing values

df.dropna(axis = 1)# Directly delete columns with missing values

df.dropna(how = "all")# Delete only rows with all missing values

df.dropna(thresh = 4)# Keep at least 4 Rows with missing values

df.dropna(subset = ["C"])# Delete specific columns with missing values

Fill in missing values

Numerical value (Numerical Data)

Method 1 ：fillna() function

df.fillna(0)： use 0 fill

df.fillna(method="pad")： Fill with the previous value

df.fillna(df2.mean())： Fill with the column mean

Method 2 ：Imputer

from sklearn.preprocessing import Imputerimr = Imputer(missing_values="NaN",
strategy="mean", axis=0)# Mean fill missing value imr = imr.fit(df)imputed_data =
imr.transform(df.values)1

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