Pandas: Convert Month Number to Month Name and vice versa

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Note: if you need to convert month name to month number, click on the last subheading.

# Pandas: Convert Month Number to Month Name

To convert month number to month name in Pandas:

Use the pandas.to_datetime() method to convert the column to a datetime object.
Use the dt.month_name() method to get the corresponding month names.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': [1, 2, 4, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

df['start_month'] = pd.to_datetime(
    df['start_month'], format='%m').dt.month_name()

#     name start_month  salary
# 0  Alice     January   175.1
# 1  Bobby    February   180.2
# 2   Carl       April   190.3
# 3    Dan        July   205.4
# 4  Ethan   September   210.5
print(df)

convert month number to month name in pandas

The code for this article is available on GitHub

The pandas.to_datetime() method converts the supplied argument to datetime.

We also passed the format argument to specify that the values in the column are month numbers.

main.py

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df['start_month'] = pd.to_datetime(
    df['start_month'], format='%m').dt.month_name()

#     name start_month  salary
# 0  Alice     January   175.1
# 1  Bobby    February   180.2
# 2   Carl       April   190.3
# 3    Dan        July   205.4
# 4  Ethan   September   210.5
print(df)

You can read more about format strings in this section of the docs.

The last step is to use the dt.month_name() method to get the month names.

# Convert month number to month name (abbreviation) in DataFrame

If you only want to get the abbreviation (first 3 letters), use the str.slice() method after calling month_name().

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': [1, 2, 4, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

df['start_month'] = pd.to_datetime(
    df['start_month'], format='%m'
).dt.month_name().str.slice(stop=3)

#     name start_month  salary
# 0  Alice         Jan   175.1
# 1  Bobby         Feb   180.2
# 2   Carl         Apr   190.3
# 3    Dan         Jul   205.4
# 4  Ethan         Sep   210.5
print(df)

convert month number to month abbreviation in dataframe

The code for this article is available on GitHub

The code sample uses the str.slice() method to only get the first 3 letters of each month's name.

You can also use string slicing to achieve the same result.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': [1, 2, 4, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

df['start_month'] = pd.to_datetime(
    df['start_month'], format='%m'
).dt.month_name().str[:3]

#     name start_month  salary
# 0  Alice         Jan   175.1
# 1  Bobby         Feb   180.2
# 2   Carl         Apr   190.3
# 3    Dan         Jul   205.4
# 4  Ethan         Sep   210.5
print(df)

The syntax for string slicing is string[start:stop:step].

We omitted the start index, so the slice starts at index 0 and goes up to, but not including index 3.

# Pandas: Convert Month Number to Month Name using `calendar`

You can also use the built-in calendar module to convert month number to month name in a Pandas DataFrame.

main.py

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import calendar
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': [1, 2, 4, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

df['start_month'] = df['start_month'].apply(
    lambda x: calendar.month_name[x]
)

#     name start_month  salary
# 0  Alice     January   175.1
# 1  Bobby    February   180.2
# 2   Carl       April   190.3
# 3    Dan        July   205.4
# 4  Ethan   September   210.5
print(df)

The code for this article is available on GitHub

The calendar.month_name() attribute is an array that represents the months of the year in the current locale.

main.py

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import calendar

# ['', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
print(list(calendar.month_name))

Notice that January is month number (index) 1, so the array has 13 elements where the first element is an empty string.

# Pandas: Convert Month Number to Month Abbreviation using `calendar`

You can use the calendar.month_abbr() method to convert a month number to month abbreviation.

main.py

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import calendar
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': [1, 2, 4, 7, 9],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

df['start_month'] = df['start_month'].apply(
    lambda x: calendar.month_abbr[x]
)

#     name start_month  salary
# 0  Alice         Jan   175.1
# 1  Bobby         Feb   180.2
# 2   Carl         Apr   190.3
# 3    Dan         Jul   205.4
# 4  Ethan         Sep   210.5
print(df)

The code for this article is available on GitHub

The calendar.month_abbr attribute returns an array containing all month abbreviations (first 3 letters).

main.py

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import calendar

# ['', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
print(list(calendar.month_abbr))

Notice that Jan is the second array element (index 1).

# Convert Month name to Month number in Pandas

You can also use the calendar module if you need to convert month name to month number in Pandas.

main.py

Copied!
import calendar
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': ['Jan', 'Feb', 'Apr', 'Jul', 'Sep'],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

month_dict = dict((v, k) for k, v in enumerate(calendar.month_abbr))

print(month_dict)

print('-' * 50)

df['start_month'] = df['start_month'].map(month_dict)
print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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{'': 0, 'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6, 'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
--------------------------------------------------
    name  start_month  salary
0  Alice            1   175.1
1  Bobby            2   180.2
2   Carl            4   190.3
3    Dan            7   205.4
4  Ethan            9   210.5

We used the calendar.month_abbr attribute to construct a dictionary containing the month abbreviations and numbers.

The last step is to call the map() method on the column, passing it the dictionary.

If your DataFrame column stores the entire names of the months and not just the abbreviations, use the calendar.month_name attribute when constructing the dictionary.

main.py

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import calendar
import pandas as pd


df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'],
    'start_month': ['January', 'February', 'April', 'July', 'September'],
    'salary': [175.1, 180.2, 190.3, 205.4, 210.5],
})

month_dict = dict((v, k) for k, v in enumerate(calendar.month_name))

print(month_dict)

print('-' * 50)

df['start_month'] = df['start_month'].map(month_dict)
print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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{'': 0, 'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June': 6, 'July': 7, 'August': 8, 'September': 9, 'October': 10, 'November': 11, 'December': 12}
--------------------------------------------------
    name  start_month  salary
0  Alice            1   175.1
1  Bobby            2   180.2
2   Carl            4   190.3
3    Dan            7   205.4
4  Ethan            9   210.5

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Pandas: Convert Month Number to Month Name and vice versa

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