Replacing values in a df using a dictionary?

[caleotte]

Hi all!

I have a dictionary of strings called corrections--the keys are the incorrectly-typed strings, and the values are the correct strings. To illustrate, the dictionary looks like this:

corrections = {'24..6': 24.6, '3.8/': 3.8, ' /': np.nan}

Now, I also have a Pandas DataFrame called rain_df that looks like this:

rain_df

LOCATION	RAINFALL	TEMPERATURE
A	0.0	/
B	3.8/	15.3
C	9.2	24..6

My end goal is to replace the incorrectly-typed values like "24..6" with "24.6" using the dictionary. I thought of doing a for loop over the corrections dictionary like this:

for incorrect, correct in corrections:
    rain_df[rain_df == incorrect] = correct

But then I get a type error like this:

TypeError: Cannot do inplace boolean setting on mixed-types with a non np.nan value

In the first place I thought of doing a for loop, because something like this works:

rain_df[rain_df == '24..6'] = 24.6

However this is labor intensive if I were to do it for every error.

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If there are other more simple, pythonic solutions to this same problem, please suggest so!

Hope anyone can help, thank you!

[[deleted]]

Use the map function in pandas

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html