[FIXED] Left align the first column and center align the other columns in a Pandas table

Issue

I am trying to format a Pandas table. I want to left align the first column and center align the other two columns in a Pandas table (except for the table header).

Code:

import pandas as pd
from IPython.display import HTML
df = pd.DataFrame({'Unit': ['Bit', 'Nibble','Byte/Octet', 'Kilobyte', 'Megabyte', 'Gigabyte', 'Terabyte'], 'Abbreviation': ['b', '-', 'B', 'KB', 'MB', 'GB', 'TB'], 'Storage': ['Binary digit, single 0 or 1', '4 bits', '8 bits', '1024 bytes', '1024 KB', '1024 MB', '1024 GB']})
df.style.set_properties(**{'text-align': 'center'})

I'm using Google Colab to render the table.

Results:

This table is almost what I want. I'm trying to print the same table except that I want to hide the index numbers and left align the first column, so that I have a table with 7 rows + 1 header and 3 columns that looks like this.

I tried using df.style.hide_index() to hide the index numbers and it worked, but I couldn't figure out how to combine df.style.hide_index() with df.style.set_properties(**{'text-align': 'center'}) The first screenshot shows all three columns are center aligned and I want to left align the first column and center align the 2nd and 3rd columns, except for the header which should have all three columns center aligned.

Solution

The table can be pretty formatted in Pandas by assembling the two missing formatting conditions into a single df. I made the following two changes to the original code.

1. Hide index numbers with hide_index()

   df[["Unit", "Abbreviation", "Storage"]].style.hide_index()  
   

2. To apply to a subset of columns, you can use the subset parameter. Left align the first column by default and center align the other 2 columns using the subset parameter.

   set_properties(subset=["Abbreviation", "Storage"], **{'text-align': 'center'})
   

Code:

import pandas as pd
from IPython.display import HTML
df = pd.DataFrame({'Unit': ['Bit', 'Nibble','Byte/Octet', 'Kilobyte', 'Megabyte', 'Gigabyte', 'Terabyte'], 'Abbreviation': ['b', '-', 'B', 'KB', 'MB', 'GB', 'TB'], 'Storage': ['Binary digit, single 0 or 1', '4 bits', '8 bits', '1024 bytes', '1024 KB', '1024 MB', '1024 GB']})
df[["Unit", "Abbreviation", "Storage"]].style.hide_index().set_properties(subset=["Abbreviation", "Storage"], **{'text-align': 'center'})

Results:

Usage:

Let's say that you have an Excel spreadsheet and you want to print a custom formatted table that looks better than Excel's built-in table templates. All you need to do is open the spreadsheet in Excel and export it as a csv file. Then run the following Python code to convert the csv file to a Pandas df.

import pandas as pd
filepath = '/path/to/FILE.csv' # replace with an existing path to FILE.csv
df = pd.read_csv(filepath)
df

Use this df to make a custom formatted table.

Answered By - karel