2022-07-05
How to Convert Pandas DataFrame to Markdown Table?
- pandas
- table
- markdown
- conversion Category: note
up: MOC_Pandas
- Method 1: Directly from pandas
- Method 2: Direct use of tabulate package
- Method 3: Generate a Styled HTML Table
- References
Generating an ASCII table from a pandas DataFrame can be accomplished in several ways …
- pandas
- table
- markdown
- conversion Category: note
up: MOC_Pandas
- Method 1: Directly from pandas
- Method 2: Direct use of tabulate package
- Method 3: Generate a Styled HTML Table
- References
Generating an ASCII table from a pandas DataFrame can be accomplished in several ways. In this post, we will explore three convenient methods to help you convert pandas DataFrame to a markdown table.
Method 1: Directly from pandas
The Pandas library since version 1.0 supports markdown conversion by relying on tabulate package as optional dependency. So, remember to pip install tabulate or conda install tabulate before using to_markdown() method. Here is an example of how it works:
import pandas as pd
# prepare some data for demonstration
df = pd.DataFrame({
"weekday": ["Monday", "Thursday", "Wednesday"],
"temperature": [20, 30, 25],
"precipitation": [100, 200, 150],
}).set_index("weekday")
print(df.to_markdown())
This produces the following output:
| weekday | temperature | precipitation |
|:----------|--------------:|----------------:|
| Monday | 20 | 100 |
| Thursday | 30 | 200 |
| Wednesday | 25 | 150 |
You can also see similar example in the Pandas documentation.
Method 2: Direct use of tabulate package
The tabulate Python package is an excellent tool for generating markdown tables from pandas.DataFrame.
Here is how you can use it:
from tabulate import tabulate
print(tabulate(df, tablefmt="pipe", headers="keys"))
The output should looks identical as above:
| weekday | temperature | precipitation |
|:----------|--------------:|----------------:|
| Monday | 20 | 100 |
| Thursday | 30 | 200 |
| Wednesday | 25 | 150 |
Method 3: Generate a Styled HTML Table
You can also create a nicely styled table like this:
styled_df = df.style.background_gradient(subset=['temperature'],
cmap='BuGn')
This outputs a stylized DataFrame styled_df which you can then export to html and include in your markdown document:
print(styled_df.to_html())
This will produce output that you can paste to the markdown file:
<style type="text/css">
#T_7a378_row0_col0 {
background-color: #f7fcfd;
color: #000000;
}
#T_7a378_row1_col0 {
background-color: #00441b;
color: #f1f1f1;
}
#T_7a378_row2_col0 {
background-color: #65c2a3;
color: #000000;
}
</style>
<table id="T_7a378">
<thead>
<tr>
<th class="blank level0" > </th>
<th id="T_7a378_level0_col0" class="col_heading level0 col0" >temperature</th>
<th id="T_7a378_level0_col1" class="col_heading level0 col1" >precipitation</th>
</tr>
<tr>
<th class="index_name level0" >weekday</th>
<th class="blank col0" > </th>
<th class="blank col1" > </th>
</tr>
</thead>
<tbody>
<tr>
<th id="T_7a378_level0_row0" class="row_heading level0 row0" >Monday</th>
<td id="T_7a378_row0_col0" class="data row0 col0" >20</td>
<td id="T_7a378_row0_col1" class="data row0 col1" >100</td>
</tr>
<tr>
<th id="T_7a378_level0_row1" class="row_heading level0 row1" >Thursday</th>
<td id="T_7a378_row1_col0" class="data row1 col0" >30</td>
<td id="T_7a378_row1_col1" class="data row1 col1" >200</td>
</tr>
<tr>
<th id="T_7a378_level0_row2" class="row_heading level0 row2" >Wednesday</th>
<td id="T_7a378_row2_col0" class="data row2 col0" >25</td>
<td id="T_7a378_row2_col1" class="data row2 col1" >150</td>
</tr>
</tbody>
</table>
References
How to programmatically convert pandas dataframe to markdown table
To cite this article:
@article{Saf2022How,
author = {Krystian Safjan},
title = {How to Convert Pandas DataFrame to Markdown Table?},
journal = {Krystian's Safjan Blog},
year = {2022},
}