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)set 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
#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)astype float across columns pandas
In [273]: cols = df.columns.drop('id')
In [274]: df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
In [275]: df
Out[275]:
id a b c d e f
0 id_3 NaN 6 3 5 8 1.0
1 id_9 3.0 7 5 7 3 NaN
2 id_7 4.0 2 3 5 4 2.0
3 id_0 7.0 3 5 7 9 4.0
4 id_0 2.0 4 6 4 0 2.0
In [276]: df.dtypes
Out[276]:
id object
a float64
b int64
c int64
d int64
e int64
f float64
dtype: objectastype float across columns pandas
cols = df.columns[df.dtypes.eq('object')]Copyright © 2021 Codeinu
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