convert column to numeric pandas
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)convert column to numeric pandas
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)change dataframe column type
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: objectset dtype for multiple columns pandas
import pandas as pd
df = pd.DataFrame({'id':['a1', 'a2', 'a3', 'a4'],
'A':['0', '1', '2', '3'],
'B':['1', '1', '1', '1'],
'C':['0', '1', '1', '0']})
df[['A', 'B', 'C']] = df[['A', 'B', 'C']].apply(pd.to_numeric, axis = 1)pandas change multiple column types
#Two ways to do this
df[['parks', 'playgrounds', 'sports']].apply(lambda x: x.astype('category'))
cols = ['parks', 'playgrounds', 'sports', 'roading']:
public[cols] = public[cols].astype('category')pandas change multiple column types
#Method 1:
df["Delivery Charges"] = df[["Weight", "Package Size", "Delivery Mode"]].apply(
lambda x : calculate_rate(*x), axis=1)
#Method 2:
df["Delivery Charges"] = df.apply(
lambda x : calculate_rate(x["Weight"],
x["Package Size"], x["Delivery Mode"]), axis=1)Copyright © 2021 Codeinu
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