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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.
Last Updated: 31 Mar 2023

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TABLE OF CONTENTS

 * Objective For ‘How To Append Output Of For Loop in Python Dataframe’
 * When Should You Append Output Of for Loop in a Python Dataframe?
 * Steps To Append Output of For Loop in a Python Dataframe
 * How To Append Rows To Pandas Dataframe in ‘for’ Loop?
 * How To Append Column To Pandas Dataframe in ‘for’ Loop?
 * How To Append List To Pandas Dataframe in Loop?
 * How To Append To Empty Pandas Dataframes in 'for' Loop?
 * FAQs on Append Output Of for Loop in Python Dataframe


OBJECTIVE FOR ‘HOW TO APPEND OUTPUT OF FOR LOOP IN PYTHON DATAFRAME’

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.




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'])

 2. 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}

 3. Append each dictionary to a list.

dict_list.append(row_dict)

 4. 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)




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 = []

 2. 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)

 3. 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)




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'])

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

for i in range(5):

    my_list = [i, i**2]

 3. 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)

 4. 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'])

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

for i in range(5):

  row = [i, i**2]

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

 df.loc[len(df)] = row

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

df = df.reset_index(drop=True)




FAQS ON APPEND OUTPUT OF FOR LOOP IN PYTHON DATAFRAME


 1. HOW TO APPEND DATA IN FOR LOOP IN PYTHON?

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.


 2. HOW TO PUT THE RESULTS OF LOOP INTO A DATAFRAME PYTHON?

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.


 3. HOW DO I APPEND TO A PANDAS DATAFRAME IN A LOOP?

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.


 4. CAN YOU LOOP THROUGH A PANDAS DATAFRAME?

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

 

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