search
Search
Login
Unlock 100+ guides
menu
menu
web
search toc
close
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
What does this mean?
Why is this true?
Give me some examples!
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
icon_star
Doc Search
icon_star
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Navigate to
check_circle
Mark as learned
thumb_up
7
thumb_down
0
chat_bubble_outline
0
Comment
auto_stories Bi-column layout
settings

Converting K and M to numerical form in Pandas DataFrame

schedule Aug 12, 2023
Last updated
local_offer
PythonPandas
Tags
tocTable of Contents
expand_more
mode_heat
Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!

Consider the following DataFrame:

df = pd.DataFrame({"A":["20K","2.5K","30M","3.5M","500"]})
df
A
0 20K
1 2.5K
2 30M
3 3.5M
4 500

Here, column A is of type string.

Solution

To convert "K" (thousand) and "M" (million) to numerical form:

df["A"].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(int)
0 20000
1 2500
2 30000000
3 3500000
4 500
Name: A, dtype: int64

Explanation

We first use the replace(~) method to replace K and M with *1e3 and *1e6, respectively:

df["A"].replace({"K":"*1e3", "M":"*1e6"}, regex=True)
0 20*1e3
1 2.5*1e3
2 30*1e6
3 3.5*1e6
4 500
Name: A, dtype: object

Note the following:

  • regex=True is needed if we want the key string to be replaced by value string (e.g. K replaced by "*1e3" in this case)

  • 1e3 is the scientific notation of 1000.

Next, we mathematically evaluate each value using map(pd.eval):

df["A"].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval)
0 20000.0
1 2500.0
2 30000000.0
3 3500000.0
4 500.0
Name: A, dtype: float64

Here, the Series' map(~) method applies the pd.eval(~) method to each of the values.

Finally, we convert all the values into integer using astype(int):

df["A"].replace({"K":"*1e3", "M":"*1e6"}, regex=True).map(pd.eval).astype(int)
0 20000
1 2500
2 30000000
3 3500000
4 500
Name: A, dtype: int64
robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
7
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!