print("\nIterating over rows using loc function :\n") # iterate through each row and select # 'Name' and 'Age' column respectively. for i in range(len(df)): print(df.loc[i, "Name"], df.loc[i, "Age"]) 输出 Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 ...
print("\nIterating over rows using loc function :\n") # iterate through each row andselect#'Name'and'Age'column respectively.foriinrange(len(df)) : print(df.loc[i,"Name"], df.loc[i,"Age"]) 输出: Given Dataframe : Name Age Stream Percentage0Ankit21Math881Amit19Commerce922Aishwarya20...
1. 使用Dataframe的index属性 # import pandas package as pdimportpandasaspd# Define a dictionary containing students datadata={'Name':['Ankit','Amit','Aishwarya','Priyanka'],'Age':[21,19,20,18],'Stream':['Math','Commerce','Arts','Biology'],'Percentage':[88,92,95,70]}# Convert the ...
加载这个文件后,我们可以遍历每一行,并用'type'列将数据类型赋值给'feature'列中定义的变量名称。 #Iterate through each row and assign variable type.#Note: astype is used to assign typesfori,rowincolTypes.iterrows():#i: dataframe index; row: each row in series formatifrow['type']=="categorical...
#Iterate through each row and assign variable type. #Note: astype is used to assign types for i, row in colTypes.iterrows(): #i: dataframe index; row: each row in series format if row['type']=="categorical": data[row['feature']]=data[row['feature']].astype(np.object) elif row...
Here, we usediterrows()to iterate through each row. Based on the value in theScorescolumn for each row, we determined the grade. We then set the grade in theGradecolumn for the current row using the.ataccessor. However, it's important to note that modifying a DataFrame within a loop us...
#Iterate through each row and assign variable type.#Note: astype is used to assign typesfor i, row in colTypes.iterrows(): #i: dataframe index; row: each row in series format if row['type']=="categorical": data ]=data ].astype(np.object) elif row['type']=="continuous": data ...
The iterrows() function offers the flexibility to sophisticatedly iterate through these rows of the dataframe. So every row of the iterrows() functions are iterated through a compact for loop and printed on to the console. Example #2 Code: import pandas as pd Core_Dataframe = pd.DataFrame({...
1#iterate only through rows with missing LoanAmount2fori,rowindata.loc[data['LoanAmount'].isnull(),:].iterrows():3ind=tuple([row['Gender'],row['Married'],row['Self_Employed']])4data.loc[i,'LoanAmount']=impute_grps.loc[ind].values[0]56#Now check the #missing values again to co...
This is becauseiterrows()returns a tuple of the format:[Hashable, Series]for each row it iterates through. While theHashableholds the row's index label, theSeriesholds the row's data. The proper use of theiterrowsrequires us to unpack it like so: ...