Preparation
Before any data manipulation can occur, two (2) new libraries will require installation.
- The Pandas library enables access to/from a DataFrame.
- The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.
To install these libraries, navigate to an IDE terminal. At the command prompt ($
), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($
). Your terminal prompt may be different.
$ pip install pandas
Hit the <Enter>
key on the keyboard to start the installation process.
$ pip install numpy
Hit the <Enter>
key on the keyboard to start the installation process.
If the installations were successful, a message displays in the terminal indicating the same.
FeFeel free to view the PyCharm installation guide for the required libraries.
Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.
import pandas as pd import numpy
DataFrame append()
The append()
method adds rows to the bottom (end) of a DataFrame/Series. A new DataFrame/Series returns with the appropriate rows appended. Columns not existing in the calling object add as new column(s).
The syntax for this method is as follows:
DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)
Parameter | Description |
---|---|
other | This parameter can be a DataFrame, Series, dictionary, or a list. These column(s) append to the original calling object. |
ignore_index | If True , ignore the original index: False use the original index. |
verify_integrity | If True, raise a ValueError if duplicates exist. |
sort | Sort the column(s) if the calling object and the other parameter do not align. |
For this example, we have two (2) DataFrames. One with existing customer login credentials and one with new customer credentials. The code below appends them to form one (1) DataFrame.
Code β Example 1
df_custs = pd.DataFrame({('jkende', 'Vzs*@4:kNq%)'), ('sarahJ', '{M$*3zB~-a-W'), ('AmyKerr', '*7#<bSt?Y_Z<')}, columns=['username', 'password'], index=['user-a', 'user-b', 'user-c']) print(df_custs) df_new = pd.DataFrame({('twilles', '&4&F#@[>g$+%'), ('cindylou', 'JBW!ktA3;9sD')}, columns=['username', 'password'], index=['user-d', 'user-e']) print(df_new) df = df_custs.append(df_new) print(df)
- Line [1] creates a DataFrame from a dictionary of tuples and assigns it to
df_custs
. - Line [2] outputs this DataFrame to the terminal.
- Line [3] creates a DataFrame from a dictionary of tuples and assigns it to
df_new
. - Line [4] outputs this DataFrame to the terminal.
- Line [5] appends the DataFrame
df_new
to the end of the DataFramedf_custs
. This output saves to a new DataFrame (df
). - Line [6] outputs this DataFrame to the terminal.
Output
df_custs
username | password | |
user-a | jkende | Vzs*@4:kNq%) |
user-b | AmyKerr | *7#<bSt?Y_Z< |
user-c | sarahJ | {M$*3zB~-a-W |
df_new
username | password | |
user-d | twilles | &4&F#@[>g$+% |
user-e | cindylou | JBW!ktA3;9sD |
df
username | password | |
user-a | jkende | Vzs*@4:kNq%) |
user-b | AmyKerr | *7#<bSt?Y_Z< |
user-c | sarahJ | {M$*3zB~-a-W |
user-d | twilles | &4&F#@[>g$+% |
user-e | cindylou | JBW!ktA3;9sD |
For this example, one (1) record is appended to the DataFrame df_custs
using loc.
Code β Example 2
df_custs = pd.DataFrame({('jkende', 'Vzs*@4:kNq%)'), ('sarahJ', '{M$*3zB~-a-W'), ('AmyKerr', '*7#<bSt?Y_Z<')}, columns=['username', 'password'], index=['user-a', 'user-b', 'user-c']) df_custs.loc['user-d'] = ('jkende', 'Vzs*@4:kNq%)') print(df_custs)
- Line [1] creates a DataFrame from a Dictionary of Tuples and assigns it to
df_custs
. - Line [2] uses
loc
to append one (1) record to the end of the DataFrame. - Line [3] outputs the DataFrame to the terminal.
Output
df_custs
username | password | |
user-a | jkende | Vzs*@4:kNq%) |
user-b | AmyKerr | *7#<bSt?Y_Z< |
user-c | sarahJ | {M$*3zB~-a-W |
updated df_custs
username | password | |
user-a | jkende | Vzs*@4:kNq%) |
user-b | AmyKerr | *7#<bSt?Y_Z< |
user-c | sarahJ | {M$*3zB~-a-W |
user-d | twilles | &4&F#@[>g$+% |
More Pandas DataFrame Methods
Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:
Also, check out the full cheat sheet overview of all Pandas DataFrame methods.