CODEWITHSUNDEEP
pythonpandasdataframe
MZG
MZG

Reputation: 335

Check if a column contains object containing float values in pandas data frame

Suppose I have a column that contains object which contains float(like this "12.45"). How can I check if this column exists in panda data frame. I want to find this column first and convert all of it's values into float. In another words how can I look for the columns that have objects containing numeric values like floats "12.54". I do not want to look for the objects that contain non numeric values like "cat" or "dog"

for example:

import pandas as pd

df = pd.DataFrame({"column1":[ "12.44", "56.78", "45.87"],
    "column2":["cat", "dog", "horse"]})

I want to check if column1 exists in my columns in order to convert all of it's values to float

Upvotes: 3

Views: 6281

Answers (2)

SeaBean
SeaBean

Reputation: 23217

You can use pd.to_numeric to try to convert the strings to numeric values. Then, check for each element in the converted columns whether it is an instance of float type by using .applymap() and isinstance(x, float). Finally, check for any column value in a column is of type float by .any():

df.apply(pd.to_numeric, errors="ignore").applymap(lambda x: isinstance(x, float), na_action='ignore').any()

Result:

column1     True
column2    False
dtype: bool

True value of column1 corresponds to it has at least one element of type float

Actually, this solution can also check for individual elements whether of float type by removing the .any() at the end:

df.apply(pd.to_numeric, errors="ignore").applymap(lambda x: isinstance(x, float), na_action='ignore')

Result:

   column1  column2
0     True    False
1     True    False
2     True    False

Upvotes: 3

tdelaney
tdelaney

Reputation: 77337

You can try to convert column by column and catch errors along the way. Columns that won't convert are unchanged because the exception is raised before the column is reassigned.

import pandas as pd

df = pd.DataFrame({"column1":[ "12.44", "56.78", "45.87"],
    "column2":["cat", "dog", "horse"],
    "column3":["1", "2", "3"]})

for colname in df.columns:
    try:
        df[colname] = df[colname].astype('float')
        print(f"{colname} converted")
    except ValueError:
        print(f"{colname} failed")
    
print(df.dtypes)

Upvotes: 0

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