This tutorial will discuss how to iterate over rows or columns of a DataFrame by index positions or label names.
Table Of Contents
- Iterate over rows of a DataFrame by index labels
- Iterate over rows of a DataFrame by index Positions
- Iterate over columns of DataFrame using Column Names
- Iterate over columns of DataFrame by column numbers
First, we will create a DataFrame,
import pandas as pd
# List of Tuples
empoyees = [(11, 'jack', 34, 'Sydney', 5) ,
(12, 'Riti', 31, 'Delhi' , 7) ,
(13, 'Aadi', 16, 'New York', 11) ,
(14, 'Mohit', 32,'Delhi' , 15) ,
(15, 'Veena', 33, 'Delhi' , 4) ,
(16, 'Shaunak', 35, 'Mumbai', 5 ),
(17, 'Shaun', 35, 'Colombo', 11)]
# Create a DataFrame object
df = pd.DataFrame( empoyees,
columns=['ID', 'Name', 'Age', 'City', 'Experience'],
index=['a', 'b', 'c', 'd', 'e', 'f', 'h'])
# Display the DataFrame
print(df)
Output:
ID Name Age City Experience a 11 jack 34 Sydney 5 b 12 Riti 31 Delhi 7 c 13 Aadi 16 New York 11 d 14 Mohit 32 Delhi 15 e 15 Veena 33 Delhi 4 f 16 Shaunak 35 Mumbai 5 h 17 Shaun 35 Colombo 11
This DataFrame has seven rows and five columns. Now let’s see how to iterate over this DataFrame.
Iterate over rows of a DataFrame by index labels
In Pandas, the DataFrame class provides a method iterrows(), it yields an iterator that can be used to loop over all the rows of a DataFrame. For each of the rows, it returns a tuple, which contains the index label and row contents as a Series object. From the Series object, we can use the values attribute to get the row values as a NumPy Array.
Let’s iterate over all the rows of the above-created dataframe using iterrows() i.e.
Frequently Asked:
# Iterate over rows of DataFrame by Index Labels
for (index_label, row_series) in df.iterrows():
print('Row Index label : ', index_label)
print('Row Content as NumPy Array: ', row_series.values)
Output:
Row Index label : a Row Content as NumPy Array: [11 'jack' 34 'Sydney' 5] Row Index label : b Row Content as NumPy Array: [12 'Riti' 31 'Delhi' 7] Row Index label : c Row Content as NumPy Array: [13 'Aadi' 16 'New York' 11] Row Index label : d Row Content as NumPy Array: [14 'Mohit' 32 'Delhi' 15] Row Index label : e Row Content as NumPy Array: [15 'Veena' 33 'Delhi' 4] Row Index label : f Row Content as NumPy Array: [16 'Shaunak' 35 'Mumbai' 5] Row Index label : h Row Content as NumPy Array: [17 'Shaun' 35 'Colombo' 11]
Here, we iterated over all the rows of the DataFrame by row index labels.
Iterate over rows of a DataFrame by index Positions
Get the count of the number of rows in the DataFrame. Then loop through 0 to N, where N is the number of rows in the DataFrame. During iteration, access each row as a Series object by the index position using iloc[]. From the Series object, use the values attribute to get the row values as a NumPy Array.
# Iterate over rows of DataFrame by index positions
for i in range(0, df.shape[0]):
print('Row Index Position : ', i)
# Get row contents as NumPy Array from Series
rowContent = df.iloc[i].values
print('Row Content as NumPy Array: ', rowContent)
Output:
Row Index Position : 0 Row Content as NumPy Array: [11 'jack' 34 'Sydney' 5] Row Index Position : 1 Row Content as NumPy Array: [12 'Riti' 31 'Delhi' 7] Row Index Position : 2 Row Content as NumPy Array: [13 'Aadi' 16 'New York' 11] Row Index Position : 3 Row Content as NumPy Array: [14 'Mohit' 32 'Delhi' 15] Row Index Position : 4 Row Content as NumPy Array: [15 'Veena' 33 'Delhi' 4] Row Index Position : 5 Row Content as NumPy Array: [16 'Shaunak' 35 'Mumbai' 5] Row Index Position : 6 Row Content as NumPy Array: [17 'Shaun' 35 'Colombo' 11]
Here, we looped through all the rows of the DataFrame by the index positions.
Iterate over columns of DataFrame using Column Names
In Pandas, the Dataframe provides attribute columns, which give a sequence of column names. We can iterate over these column names, and for each column label, we can select the column contents as a Series object using the subscript operator ( [] ). From the Series object, use the values attribute to get the column values as a NumPy Array. For example,
# Iterate over the sequence of column names
for column in df.columns:
# Select column contents by column name using [] operator
columnSeriesObj = df[column]
print('Colunm Name : ', column)
print('Column Contents as NumPy Array: ', columnSeriesObj.values)
Output:
Colunm Name : ID Column Contents as NumPy Array: [11 12 13 14 15 16 17] Colunm Name : Name Column Contents as NumPy Array: ['jack' 'Riti' 'Aadi' 'Mohit' 'Veena' 'Shaunak' 'Shaun'] Colunm Name : Age Column Contents as NumPy Array: [34 31 16 32 33 35 35] Colunm Name : City Column Contents as NumPy Array: ['Sydney' 'Delhi' 'New York' 'Delhi' 'Delhi' 'Mumbai' 'Colombo'] Colunm Name : Experience Column Contents as NumPy Array: [ 5 7 11 15 4 5 11]
Here, we looped through all the columns of the DataFrame by the column names.
Iterate over columns of DataFrame by column numbers
To iterate over the columns of a DataFrame by column numbers,
- Get the count of total columns in the DataFrame.
- Loop over 0 to N, where N stands for the count of the number of columns
- Select each column by index position/number during iteration using iloc[].
Let’s see how to iterate over all columns of a DataFrame by column numbers,
# Iterate over columns of DataFrame by index positions
for i in range(0, df.shape[1]):
print('Colunm Number/Position: ', i)
# Get column contents as NumPy Array
columnContent = df.iloc[:, i].values
print('Column contents: ', columnContent)
Output:
Colunm Number/Position: 0 Column contents: [11 12 13 14 15 16 17] Colunm Number/Position: 1 Column contents: ['jack' 'Riti' 'Aadi' 'Mohit' 'Veena' 'Shaunak' 'Shaun'] Colunm Number/Position: 2 Column contents: [34 31 16 32 33 35 35] Colunm Number/Position: 3 Column contents: ['Sydney' 'Delhi' 'New York' 'Delhi' 'Delhi' 'Mumbai' 'Colombo'] Colunm Number/Position: 4 Column contents: [ 5 7 11 15 4 5 11]
Here, we looped through all the columns of the DataFrame by the column index numbers.
Summary:
We learned about the different ways to iterate over all rows or columns of a DataFrame by label names or by index positions.
