1. Filter out missing data dropna()
import pandas as pd
import numpy as np
df=({"record":[,"Sub-healthy|Pan Light|45 years old","Disease | Jang Si",],"date":[,20210102,20210103,20210104]},index=["one","two","three","four"])
1) Filter all rows containing NaN values
()# Default axis=0
2) Filter out all columns containing NaN values
(axis=1)
3) Filter rows whose elements are all NaN values
(axis=0,how="all")
4) Filter columns whose elements are all NaN values
5) Filter out missing rows in the specified columns.
(subset=["record"],axis=0)
If you need to make changes directly on the original data, you need to set the parameter inplace=True.
2. Delete duplicate values drop_duplicates()
df=({'state':[1,1,2,2,1,2,2],'pop':['a','b','c','d','b','c','d']})
Syntax: drop_duplicates(subset,keep,inplace), where argument keep:{'first','last', False}, defaults to 'first'
first: keep the first occurrence of duplicates and delete the second and subsequent occurrences.
last: retains the last occurrence of duplicates and removes duplicates that occurred before.
"false": removes all duplicates.
1)keep=“first”
df.drop_duplicates(keep="first")
2)keep=“last”
df.drop_duplicates(keep="last")
3)keep=False
df.drop_duplicates(keep=False)
4) Delete the row corresponding to the duplicate item in the specified column.
df.drop_duplicates(subset=["state"],keep="first")
If you need to make changes directly on the original data, you need to set the parameter inplace=True.
3. Delete rows and columns according to the specified conditions drop()
df=((16).reshape(4,4),columns=["one","two","three","four"])
1). Delete the specified column
(["one"],axis=1)
Alternatively, thedel df["one"]to remove the specified column, but this method is not recommended because it defaults to making changes directly on the source data.
2). Delete the specified line
([0],axis=0)
If you need to make changes directly on the original data, you need to set the parameter inplace=True.
summarize
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