df[df.notnull()]df.dropna()#将所有含有nan项的row删除df.dropna(axis=1,thresh=3)#将在列的方向上三个为NaN的项删除df.dropna(how='ALL')#将全部项都是nan的row删除123456此处:printdata.dropna()和printdata[data.notnull()]结果一样 填充无效值 df.fillna(0)df.fillna({1:0,2:0.5})#对第一列...
df[df.notnull()] df.dropna() #将所有含有nan项的row删除 df.dropna(axis=1,thresh=3) #将在列的方向上三个为NaN的项删除 df.dropna(how='ALL') #将全部项都是nan的row删除 这里面,print(data.dropna() )和 print(data[data.notnull()] )结果一样 填充空缺项 df.fillna(0) df.fillna({1:0,...
# Remove Rows by Slicing DataFramedf2=df[4:]# Returns rows from 4th rowdf2=df[1:-1]# Removes first and last rowdf2=df[2:4]# Return rows between 2 and 4 Related:You can alsoremove first N rows from pandas DataFrameandremove last N Rows from pands DataFrame ...
Pandas Drop the First Row using iloc[] To drop the first row usingiloc[], you can specify the index range from the second row onwards. For instance, useDataFrame.iloc[1:]to select all rows from index position 1 (inclusive) onwards. By specifying[1:], you’re effectively excluding the ...
player_df.dropna(inplace=True, how='all') player_df.isna().sum() Output Copy ID 0 points 3 possessions 3 team_pace 3 GP 7 MPG 6 TS% 1 AST 1 TO 1 USG 1 ORR 1 DRR 1 REBR 1 PER 10 dtype: int64 Hmmm. No change. So you know that no row has all NaN values. That's...
argument. In such cases, we use theaxisparameter to decide if we want to drop a row or a column. If we want to drop a column from the dataframe, we set theaxisparameter to 1. When we want to drop a row from the dataframe, we set theaxisparameter to 0 which is its default ...
Pandas version checks I have checked that this issue has not already been reported. I have confirmed this bug exists on the latest version of pandas. I have confirmed this bug exists on the main branch of pandas. Reproducible Example df ...
#Additional Resources You can learn more about the related topics by checking out the following tutorials: I wrotea bookin which I share everything I know about how to become a better, more efficient programmer. Search for posts 0 ... ... .....
DataFrame.drop( labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise' ) # short forms df.drop(axis=None) # deletes a specified column df.drop(index=None) # deletes a specified row Let us understand with the help of an example. ...
Delete Pandas DataFrame row based on multiple conditions By: Rajesh P.S.You can delete DataFrame rows based on a condition using boolean indexing. By creating a boolean mask that selects the rows that meet the condition, you can then use the drop method to delete those rows from the ...