www.pythonforbeginners.com Open in urlscan Pro
2a06:98c1:3120::3  Public Scan

Form analysis
3 forms found in the DOM

GET https://www.pythonforbeginners.com/

<form class="search-form" method="get" action="https://www.pythonforbeginners.com/" role="search"><label class="search-form-label screen-reader-text" for="searchform-1">Search this website</label><input class="search-form-input" type="search"
    name="s" id="searchform-1" placeholder="Search this website"><input class="search-form-submit" type="submit" value="Search">
  <meta content="https://www.pythonforbeginners.com/?s={s}">
</form>

POST

<form id="free-guide-to-learning-python-1" class="yikes-easy-mc-form yikes-easy-mc-form-1 sidebar_email_form" method="POST" data-attr-form-id="1"> <label for="yikes-easy-mc-form-1-EMAIL" class="EMAIL-label yikes-mailchimp-field-required "> <span
      class="EMAIL-label"> Email Address </span> <input id="yikes-easy-mc-form-1-EMAIL" name="EMAIL" placeholder="" class="yikes-easy-mc-email " required="required" type="email" value=""> </label> <label for="yikes-easy-mc-form-1-SOURCE"
    style="display:none;" class="SOURCE-label "> <span class="SOURCE-label"> source </span> <input id="yikes-easy-mc-form-1-SOURCE" name="SOURCE" placeholder="" class="yikes-easy-mc-text " type="text" value="Sidebar"> </label> <input type="hidden"
    name="yikes-mailchimp-honeypot" id="yikes-mailchimp-honeypot-1" value=""> <input type="hidden" name="yikes-mailchimp-associated-list-id" id="yikes-mailchimp-associated-list-id-1" value="544dbfab57"> <input type="hidden"
    name="yikes-mailchimp-submitted-form" id="yikes-mailchimp-submitted-form-1" value="1"> <button type="submit" class="yikes-easy-mc-submit-button yikes-easy-mc-submit-button-1 btn btn-primary sidebar_email_form_button"> <span
      class="yikes-mailchimp-submit-button-span-text">Subscribe</span></button> <input type="hidden" id="yikes_easy_mc_new_subscriber_1" name="yikes_easy_mc_new_subscriber" value="970c8844ae"> <input type="hidden" name="_wp_http_referer"
    value="/basics/append-dictionary-to-dataframe-in-python"></form>

<form class="g-jdoua8 g-hxd0xw" novalidate="novalidate">
  <div class="gw5f1np"><w-div class="g-p0ly7s g65zlmy"><label for="gscw_email_input" class="g-6l6pad">Email</label><w-div class="g-kmcsz7 g-qyrply"><input type="email" maxlength="1000" title="Email" placeholder="Email address" name="email"
          id="gscw_email_input" class="g-xhids6 g-b4ef17"></w-div></w-div> <button type="submit" class="g3ylmud "> <span class="gyzp9jm">Subscribe</span> <span class="g-recys7" title="Subscribe"></span></button></div>
</form>

Text Content

 * Skip to primary navigation
 * Skip to main content
 * Skip to primary sidebar

PythonForBeginners.com

Learn By Example

 * Home
 * Learn Python
   * Python Tutorial
 * Categories
   * Basics
   * Lists
   * Dictionary
   * Code Snippets
   * Comments
   * Modules
   * API
   * Beautiful Soup
   * Cheatsheet
   * Games
   * Loops
 * Python Courses
   * Python 3 For Beginners

You are here: Home / Basics / Append Dictionary to Dataframe in Python


APPEND DICTIONARY TO DATAFRAME IN PYTHON

Author: Aditya Raj
Last Updated: November 9, 2022

We use a python dictionary to store key-value pairs. Similarly, Dataframes are
used to store records containing values associated with a key in the tabular
format. In this article, we will discuss how we can append a dictionary to a
dataframe in Python.


HOW TO APPEND A DICTIONARY TO A DATAFRAME IN PYTHON?

To append a dictionary to a pandas dataframe, we will use the append() method.
The append() method, when invoked on a dataframe, takes a dictionary as its
input argument and returns a new dataframe. The append() method also takes the
value True as an input argument for its ignore_index parameter.


LATEST VIDEOS


Tech and Gaming


0 seconds of 34 secondsVolume 0%

Press shift question mark to access a list of keyboard shortcuts
Keyboard ShortcutsEnabledDisabled
Play/PauseSPACE
Increase Volume↑
Decrease Volume↓
Seek Forward→
Seek Backward←
Captions On/Offc
Fullscreen/Exit Fullscreenf
Mute/Unmutem
Seek %0-9
Live
00:25
00:09
00:34






 

 The output dataframe contains the dictionary values appended as a row in the
original dataframe. You can observe this in the following example.



import pandas as pd
names=pd.read_csv("name.csv")
print("The input dataframe is:")
print(names)
myDict={"Class":3, "Roll":22, "Name":"Sakshi"}
print("The dictionary is:")
print(myDict)
names=names.append(myDict,ignore_index=True)
print("The output dataframe is:")
print(names)

Output



The input dataframe is:
   Class  Roll      Name
0      1    11    Aditya
1      1    12     Chris
2      1    13       Sam
3      2     1      Joel
4      2    22       Tom
5      2    44  Samantha
6      3    33      Tina
7      3    34       Amy
The dictionary is:
{'Class': 3, 'Roll': 22, 'Name': 'Sakshi'}
The output dataframe is:
   Class  Roll      Name
0      1    11    Aditya
1      1    12     Chris
2      1    13       Sam
3      2     1      Joel
4      2    22       Tom
5      2    44  Samantha
6      3    33      Tina
7      3    34       Amy
8      3    22    Sakshi

If a dictionary has less number of keys than the columns specified in the
dataframe, the remaining columns are assigned the value NaN in the rows where
the dictionary is appended. You can observe this in the following example.

import pandas as pd
names=pd.read_csv("name.csv")
print("The input dataframe is:")
print(names)
myDict={"Class":3, "Name":"Sakshi"}
print("The dictionary is:")
print(myDict)
names=names.append(myDict,ignore_index=True)
print("The output dataframe is:")
print(names)

Output:

The input dataframe is:
   Class  Roll      Name
0      1    11    Aditya
1      1    12     Chris
2      1    13       Sam
3      2     1      Joel
4      2    22       Tom
5      2    44  Samantha
6      3    33      Tina
7      3    34       Amy
The dictionary is:
{'Class': 3, 'Name': 'Sakshi'}
The output dataframe is:
   Class  Roll      Name
0      1  11.0    Aditya
1      1  12.0     Chris
2      1  13.0       Sam
3      2   1.0      Joel
4      2  22.0       Tom
5      2  44.0  Samantha
6      3  33.0      Tina
7      3  34.0       Amy
8      3   NaN    Sakshi

If a dictionary doesn’t have the keys same as the columns in the dataframe, a
new column is added to the dataframe for each key that is not present in the
dataframe as the column name. While appending a dictionary with key values
different from the column names, the values for the new columns in the existing
rows are filled as NaN. Similarly, the columns which are not present in the
dictionary but are present in the dataframe are assigned the value NaN is the
row where the dictionary is appended. You can observe that in the following
example.



import pandas as pd
names=pd.read_csv("name.csv")
print("The input dataframe is:")
print(names)
myDict={"Class":3, "Roll":22, "Name":"Sakshi","Height":160}
print("The dictionary is:")
print(myDict)
names=names.append(myDict,ignore_index=True)
print("The output dataframe is:")
print(names)

Output:

The input dataframe is:
   Class  Roll      Name
0      1    11    Aditya
1      1    12     Chris
2      1    13       Sam
3      2     1      Joel
4      2    22       Tom
5      2    44  Samantha
6      3    33      Tina
7      3    34       Amy
The dictionary is:
{'Class': 3, 'Roll': 22, 'Name': 'Sakshi', 'Height': 160}
The output dataframe is:
   Class  Roll      Name  Height
0      1    11    Aditya     NaN
1      1    12     Chris     NaN
2      1    13       Sam     NaN
3      2     1      Joel     NaN
4      2    22       Tom     NaN
5      2    44  Samantha     NaN
6      3    33      Tina     NaN
7      3    34       Amy     NaN
8      3    22    Sakshi   160.0

To append two or more dictionaries or a list of dictionaries to the dataframe,
you can use a for loop along with the append() method. In the for loop, you can
append each dictionary to the dataframe.

Suggested Reading: If you are into machine learning, you can read this article
on k-prototypes clustering with numerical example. You might also like this
article on data ink ratio.



The append() method will be deprecated in near future. So, you can use the
pandas concat() method to append a dictionary to the dataframe as shown below.

import pandas as pd
names=pd.read_csv("name.csv")
print("The input dataframe is:")
print(names)
myDict={"Class":3, "Roll":22, "Name":"Sakshi","Height":160}
print("The dictionary is:")
print(myDict)
tempDf=pd.DataFrame([myDict])
names=pd.concat([names,tempDf],ignore_index=True)
print("The output dataframe is:")
print(names)

Output:

The input dataframe is:
   Class  Roll      Name
0      1    11    Aditya
1      1    12     Chris
2      1    13       Sam
3      2     1      Joel
4      2    22       Tom
5      2    44  Samantha
6      3    33      Tina
7      3    34       Amy
The dictionary is:
{'Class': 3, 'Roll': 22, 'Name': 'Sakshi', 'Height': 160}
The output dataframe is:
   Class  Roll      Name  Height
0      1    11    Aditya     NaN
1      1    12     Chris     NaN
2      1    13       Sam     NaN
3      2     1      Joel     NaN
4      2    22       Tom     NaN
5      2    44  Samantha     NaN
6      3    33      Tina     NaN
7      3    34       Amy     NaN
8      3    22    Sakshi   160.0


CONCLUSION

In this article, we have discussed how to append a dictionary to a dataframe in
python. To know more about python programming, you can read this article on
dictionary comprehension in python. You might also like this article on list
comprehension in python.





RELATED

Append a New Row in a Dataframe in PythonMarch 11, 2022In "Basics"

List of Dictionaries to Dataframe in PythonJune 24, 2022In "Basics"

Pandas Replace Values in Dataframe or SeriesJanuary 9, 2023In "Basics"



Advertisement



RECOMMENDED PYTHON TRAINING

Course: Python 3 For Beginners

Over 15 hours of video content with guided instruction for beginners. Learn how
to create real world applications and master the basics.

Enroll Now

Filed Under: Basics Author: Aditya Raj


MORE PYTHON TOPICS

API Argv Basics Beautiful Soup bitly Cheatsheet Code Code Snippets Command Line
Comments Concatenation crawler Data Structures Data Types deque Development
Dictionary Dictionary Data Structure In Python Error Handling Exceptions
Filehandling Files Functions Games GUI Json Lists Loops Mechanzie Modules
Modules In Python Mysql OS pip Python Python On The Web Python Strings Queue
Requests Scraping Scripts Split Strings System & OS urllib2


PRIMARY SIDEBAR

Search this website


MENU

 * Basics
 * Cheatsheet
 * Code Snippets
 * Development
 * Dictionary
 * Error Handling
 * Lists
 * Loops
 * Modules
 * Scripts
 * Strings
 * System & OS
 * Web




GET OUR FREE GUIDE TO LEARNING PYTHON

Email Address source Subscribe


MOST POPULAR CONTENT

 * Reading and Writing Files in Python
 * Python Dictionary – How To Create Dictionaries In Python
 * How to use Split in Python
 * Python String Concatenation and Formatting
 * List Comprehensions in Python
 * How to Use sys.argv in Python?
 * How to use comments in Python
 * Try and Except in Python


RECENT POSTS

 * Convert XML to Dictionary in Python
 * Convert Python Dictionary to YAML
 * Convert YAML to JSON in Python
 * Load JSON into a Python Dictionary
 * Convert JSON to YAML in Python



Copyright © 2012–2023 · PythonForBeginners.com

 * Home
 * Contact Us
 * Privacy Policy
 * Write For Us

Update Privacy Preferences
An Elite CafeMedia Tech Publisher





Thank you!
You have successfully subscribed.
Download our Free Guide To Learning Python
Subscribe to receive our Free Guide To Learning Python!
Email Subscribe
We will not share your information with anyone
Learn Python Start to Finish
Didn't find what you were looking for? Sorry about that, but we have put
together a comprehensive Python course with everything you need. This course is
perfect for beginners and intermediate Python programmers.


Give it try!
Only $9.95