How To Append Output of For Loop in a Python Dataframe?

This recipe will show you how to append output of a for loop in a Python dataframe.

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Are you working with Python lists and struggling to keep track of the loop outputs? Here’s a quick and easy recipe that shows you how to append output of for loop in a Python dataframe. Let's dive in!

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When Should You Append Output Of for Loop in a Python Dataframe?

Appending the output of a for loop to a Python dataframe can be useful in a wide range of data analysis and processing tasks, where you need to collect data from multiple iterations of a loop and combine it for further analysis.You should append output from a for loop to a dataframe in Python in case of-

  • Data Analysis: If you are analyzing data using Python, you may be working with a large dataset that needs to be processed in sections. You can use a for loop to repeatedly iterate over these sections and append the output to a Python dataframe, allowing you to work flexibly with the entire dataset.

  • Automation: If you are automating a task using Python, you may need to generate a series of results that need to be collected in a structured way. By appending the output of a for loop to a Python dataframe, you can easily keep track of your results and process them further if needed.

  • Machine Learning: If you train a machine learning model using Python, you may need to generate multiple training data sets. You can use a for loop to generate each set and append it to a dataframe, allowing you to shuffle and split the data as needed for training quickly.

  • ​​Natural Language Processing: If you're working with text data and need to perform some analysis on each sentence or paragraph, you can use a for loop to iterate over the text data and append the results to a dataframe for your next NLP project.

  • Web Scraping: If you're scraping data from a website, you may need to use a for loop to iterate over multiple pages or search results, and append the data to a dataframe for analysis.

  • Image Processing: If you're working on image processing projects and need to perform some processing on each image, you can use a for loop to iterate over the images and append the results to a dataframe for further analysis.

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Steps To Append Output of For Loop in a Python Dataframe

Below are five quick and easy steps to append and save loop results in a Python Pandas Dataframe.

Step 1 - Import the Pandas Library

import pandas as pd

Pandas are generally used for data manipulation and analysis.

Step 2 - Create Dataframe Before Appending 

df= pd.DataFrame({'Table of 9': [9,18,27], 'Table of 10': [10,20,30]})

Let us create a dataframe containing some tables of 9 and 10.

Step 3 - Append Dataframe Using Ignore Index In a ‘for’ Loop

for i in range(4,11): df=df.append({'Table of 9':i*9,'Table of 10':i*10},ignore_index=True)

Compared to the append function in the list, it applies a bit differently for the dataframe. As soon as any dataframe gets appended using the append function, it is not reflected in the original dataframe. To store the appended data in a dataframe, we again assign it back to the original dataframe.

Step 4 - Printing The For Loop Output After Append

print('df\n',df)

You can use the print function to print the newly appended dataframe.

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Step 5 - Take a Look At The Dataset 

Once we run the above code snippet, we will see the following: (Scroll down to the ipython notebook below to see the output.)

import pandas as pd

df= pd.DataFrame({'Table of 9': [9,18,27],

        'Table of 10': [10,20,30]})

for i in range(4,11):

    df=df.append({'Table of 9':i*9,'Table of 10':i*10},ignore_index=True)

print('df\n',df)

df

    Table of 9  Table of 10

0           9           10

1          18           20

2          27           30

3          36           40

4          45           50

5          54           60

6          63           70

7          72           80

8          81           90

9          90          100

How To Append Rows To Pandas Dataframe in ‘for’ Loop?

To append rows to a Pandas dataframe in loop, you can follow these steps:

  1. Create an empty dataframe with the desired columns using the Pandas library.

import pandas as pd

# create an empty dataframe with desired columns

df = pd.DataFrame(columns=['Column 1', 'Column 2'])

  1. Write the for loop and store the loop output in a dictionary where keys represent column names and values represent the row values.

# create an empty list to store dictionaries

dict_list = []

# write the for loop and store output in a dictionary

for i in range(5):

    row_dict = {'Column 1': i, 'Column 2': i**2}

  1. Append each dictionary to a list.

dict_list.append(row_dict)

  1. After the loop, convert the list of dictionaries to a Pandas dataframe using the "from_dict" method.

# convert list of dictionaries to pandas dataframe

df = pd.DataFrame.from_dict(dict_list)

# print the final dataframe

print(df)

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How To Append Column To Pandas Dataframe in ‘for’ Loop?

You can append a column to a Pandas dataframe in a for loop by following the steps below:

  1. Create an empty list to store the column data.

import pandas as pd

# create original dataframe

df = pd.DataFrame({'Column 1': [1, 2, 3], 'Column 2': [4, 5, 6]})

# create empty list to store new column data

new_column_data = []

  1. Write the for loop and append the column data to the list in each iteration.

# write the for loop and append column data to list

for i in range(3):

    new_column_data.append(i**2)

  1. After the loop, convert the list to a pandas series and append it to the original dataframe using the "insert" method.

# convert list to pandas series and append to dataframe

df.insert(loc=len(df.columns), column='New Column', value=new_column_data)

# print the final dataframe

print(df)

Image for How To Append Column To Pandas Dataframe in For Loop

How To Append List To Pandas Dataframe in Loop?

To append a list to a Pandas dataframe in a loop, you can use the "append" function and a dictionary that maps column names to the corresponding list values.

  1. Create an empty Pandas dataframe with the desired columns:

import pandas as pd

df = pd.DataFrame(columns=['Column 1', 'Column 2'])

  1. Write the for loop and generate each list to be appended:

for i in range(5):

    my_list = [i, i**2]

  1. Append each list to the dataframe using the "append" function and a dictionary:

    df = df.append({'Column 1': my_list[0], 'Column 2': my_list[1]}, ignore_index=True)

  1. After the loop, reset the index of the dataframe to ensure it is sequential:

df = df.reset_index(drop=True)

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How To Append To Empty Pandas Dataframes in 'for' Loop?

Here are the steps to append to an empty Pandas dataframe in a for loop:

  1. Create an empty Pandas dataframe with the desired columns:

import pandas as pd

df = pd.DataFrame(columns=['Column 1', 'Column 2'])

  1. Write the for loop and generate each row of data to be appended:

for i in range(5):

  row = [i, i**2]

  1. Append each row to the dataframe using the "loc" method:

 df.loc[len(df)] = row

  1. After the loop, reset the index of the dataframe to ensure it is sequential:

df = df.reset_index(drop=True)

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FAQs on Append Output Of for Loop in Python Dataframe

You can append data in a for loop in Python using the append method to add new data to a list or dataframe. You can initialize an empty list or dataframe before the loop and then append new data to it within the loop.

You can put the results of a loop into a Python dataframe by creating an empty dataframe, running the loop to generate the data, storing the output in a list, and then appending the list to the empty dataframe using the "append" method.

You can append to a pandas DataFrame in a loop by creating an empty DataFrame outside the loop and then using the DataFrame's ‘.append()’ method inside the loop to append rows to Pandas DataFrame one at a time.

Yes, you can loop through a pandas DataFrame using various methods such as iterrows(), itertuples(), and iteritems().

 

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