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Converting K and M to numerical form in Pandas DataFrame

Pandas
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DataFrame Cookbooks
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Data Manipulation Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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tocTable of Contents
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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
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Published by Isshin Inada
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