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Example to Drop Rows from Pandas DataFrame Based on Column Value # Importing pandas packageimportpandasaspd# Creating a dictionaryd={"Name":['Hari','Mohan','Neeti','Shaily','Ram','Umesh'],"Age":[25,36,26,21,30,33],"Gender":['Male','Male','Female','Female','Male','Male'],"Pr...
# axis=1 tells Python that we want to apply function on columns instead of rows # To delete the column permanently from original dataframe df, we can use the option inplace=True df.drop(['A', 'B', 'C'], axis=1, inplace=True)...
4. Now let's delete the column class 删除某一列 df.drop(columns='class',inplace=True) 5. Set the first 3 rows as NaN 把前3行都设置为NaN df.iloc[:4]=np.nan 我这里设置多了,注意哦 6. Delete the rows that have NaN 把包含NaN的行都删除掉 ...
import pandas as pd df = pd.read_csv("sampleData.csv") deviceInfo = df.iloc[:100] df.iloc[100:].to_csv("sampleData.csv") When performing repetitive tasks, it may be beneficial to useto_csv(...,index=None)instead of creating a new index column in the.csvfile every time. ...