Answers for "change multiple column data type pandas"

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: object

astype float across columns pandas

cols = df.columns[df.dtypes.eq('object')]