CODEWITHSUNDEEP
pythonpandasdataframe
Michal
Michal

Reputation: 259

After applying pd.to_numeric on multiple columns there is no change in columns Dtype

I'm wondering what I'm doing wrong while applying pd.to_numeric on multiple columns in dataframe

df_weather = pd.read_csv ('https://raw.githubusercontent.com/MichalLeh/Edinburgh-bikes-project/main/edinburgh_weather.csv')#("J:/edinburgh_weather.csv")

Sample of dataframe:

   time     temp    feels   wind            gust    rain    humidity cloud  pressure  vis       date
0   00:00   11 °c   11 °c   9 km/h from S   19 km/h 0.0 mm  79%      13%    1020 mb   Excellent 2018-09-01

First I get rid of unwanted characters:

df_weather = (df_weather[['time', 'date', 'temp', 'feels', 'wind', 'gust', 'rain', 'humidity', 'cloud', 'pressure']]
       .replace(to_replace ='[^0-9\:\-\.]', value = '', regex = True))

And then I apply to_numeric:

df_weather[['temp', 'feels', 'wind', 'gust', 'rain', 'humidity', 'cloud', 'pressure']].apply(lambda x: pd.to_numeric(x, errors='coerce'))
df_weather.info()

I'm not getting any errors and yet the result looks like this:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6336 entries, 0 to 6335
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype 
---  ------    --------------  ----- 
 0   time      6336 non-null   object
 1   temp      6336 non-null   object
 2   feels     6336 non-null   object
 3   wind      6336 non-null   object
 4   gust      6336 non-null   object
 5   rain      6336 non-null   object
 6   humidity  6336 non-null   object
 7   cloud     6336 non-null   object
 8   pressure  6336 non-null   object
 9   vis       6336 non-null   object
 10  date      6336 non-null   object
dtypes: object(11)
memory usage: 544.6+ KB

BTW pd.to_numeric works when I transform given columns one by one though. I'd love to be able to convert given data at same time. Thank you.

Upvotes: 1

Views: 1400

Answers (1)

jezrael
jezrael

Reputation: 862511

You need assign back columns converted to numeric:

cols = ['temp', 'feels', 'wind', 'gust', 'rain', 'humidity', 'cloud', 'pressure']
df_weather[cols] = df_weather[cols].apply(lambda x: pd.to_numeric(x, errors='coerce'))

Upvotes: 2

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