How to Sort Pandas Dataframe By Month Name?

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To sort a pandas dataframe by month name, you can convert the 'datetime' column to a 'CategoricalDtype' with the categories listed as month names. Then, you can use the 'sort_values' function with the 'CategoricalDtype' to sort the dataframe by month name. This will ensure that the dataframe is sorted in the order of the months.

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How to sort a dataframe by month name while preserving the original data types?

To sort a dataframe by month name while preserving the original data types, you can use the pd.Categorical() function in pandas. Here's how you can do it:

  1. Convert the month column to a categorical data type with the correct order of months.
  2. Sort the dataframe by the month column.


Here's an example:

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import pandas as pd

# Sample dataframe
data = {
    'date': ['2022-01-15', '2022-04-25', '2022-02-10', '2022-03-05'],
    'value': [10, 20, 30, 40]
}

df = pd.DataFrame(data)

# Convert the month column to categorical data type
df['month'] = pd.to_datetime(df['date']).dt.month_name()
df['month'] = pd.Categorical(df['month'], categories=[
    'January', 'February', 'March', 'April', 'May', 'June',
    'July', 'August', 'September', 'October', 'November', 'December'
], ordered=True)

# Sort the dataframe by the month column
df = df.sort_values('month')

print(df)


This will sort the dataframe by month name while preserving the original data types.


How to highlight the sorted rows in a dataframe for better visibility?

One way to highlight the sorted rows in a dataframe for better visibility is to use styling in pandas. You can apply different styles to the sorted rows to make them stand out from the rest of the dataframe.


Here is an example code snippet to highlight sorted rows in a dataframe:

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import pandas as pd

# Create a sample dataframe
data = {'A': [1, 3, 2, 4],
        'B': [5, 7, 6, 8]}
df = pd.DataFrame(data)

# Sort the dataframe by column 'A'
df_sorted = df.sort_values(by='A')

# Apply a custom style to highlight sorted rows
def highlight_sorted_rows(x):
    sorted_rows = pd.Series(0, index=x.index)
    sorted_rows.loc[x.sort_values(by='A').index] = 'background-color: yellow'
    return [sorted_rows]

df_sorted.style.apply(highlight_sorted_rows, axis=None)


In this code snippet, we first create a sample dataframe df and then sort it by column 'A' to create df_sorted. We define a custom function highlight_sorted_rows to highlight the sorted rows by changing their background color to yellow. Finally, we apply this custom style to the sorted rows using the apply method on the dataframe.


You can customize the style further by changing the background color or adding additional styling options to make the sorted rows more visible in the dataframe.


What is the default sort order for a pandas dataframe in Python?

The default sort order for a pandas dataframe in Python is ascending order.

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