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.
Feel 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 as np
DataFrame pivot()
The pivot()
method reshapes a DataFrame/Series and produces/returns a pivot table based on column values.
The syntax for this method is as follows:
DataFrame.pivot(index=None, columns=None, values=None)
Parameter | Description |
---|---|
index | This parameter can be a string, object, or a list of strings and is optional. This option makes up the new DataFrame/Series index. If None , the existing index is selected. |
columns | This parameter can be a string, object, or a list of strings and is optional. Makes up the new DataFrame/Series column(s). |
values | This parameter can be a string, object, or a list of the previous and is optional. |
For this example, we generate 3-day sample stock prices for Rivers Clothing. The column headings display the following characters.
- A (for Opening Price)
- B (for Midday Price)
- C (for Opening Price)
cdate_idx = ['01/15/2022', '01/16/2022', '01/17/2022'] * 3 group_lst = list('AAABBBCCC') vals_lst = np.random.uniform(low=0.5, high=13.3, size=(9)) df = pd.DataFrame({'dates': cdate_idx, 'group': group_lst, 'value': vals_lst}) print(df) result = df.pivot(index='dates', columns='group', values='value') print(result)
- Line [1] creates a list of dates and multiplies this by three (3). The output is three (3) entries for each date. This output saves to
cdate_idx
. - Line [2] creates a list of headings for the columns (see above for definitions). Three (3) of each character are required (9 characters). This output saves to
group_lst
. - Line [3] uses
np.random.uniform
to create a random list of nine (9) numbers between the set range. The output saves tovals_lst
. - Line [4] creates a DataFrame using all the variables created on lines [1-3]. The output saves to
df
. - Line [5] outputs the DataFrame to the terminal.
- Line [6] creates a pivot from the DataFrame and groups the data by dates. The output saves to
result
. - Line [7] outputs the result to the terminal.
Output
df
dates | group | value | |
0 | 01/15/2022 | A | 9.627767 |
1 | 01/16/2022 | A | 11.528057 |
2 | 01/17/2022 | A | 13.296501 |
3 | 01/15/2022 | B | 2.933748 |
4 | 01/16/2022 | B | 2.236752 |
5 | 01/17/2022 | B | 7.652414 |
6 | 01/15/2022 | C | 11.813549 |
7 | 01/16/2022 | C | 11.015920 |
8 | 01/17/2022 | C | 0.527554 |
result
group | A | B | C |
dates | |||
01/15/2022 | 8.051752 | 9.571285 | 6.196394 |
01/16/2022 | 6.511448 | 8.158878 | 12.865944 |
01/17/2022 | 8.421245 | 1.746941 | 12.896975 |
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.