Convert Type of Multiple Columns of a Dataframe At Once

pandas: to_numeric for multiple columns

UPDATE: you don't need to convert your values afterwards, you can do it on-the-fly when reading your CSV:

In [165]: df=pd.read_csv(url, index_col=0, na_values=['(NA)']).fillna(0)

In [166]: df.dtypes
Out[166]:
GeoName                    object
ComponentName              object
IndustryId                  int64
IndustryClassification     object
Description                object
2004                        int64
2005                        int64
2006                        int64
2007                        int64
2008                        int64
2009                        int64
2010                        int64
2011                        int64
2012                        int64
2013                        int64
2014                      float64
dtype: object

If you need to convert multiple columns to numeric dtypes - use the following technique:

Sample source DF:

In [271]: df
Out[271]:
     id    a  b  c  d  e    f
0  id_3  AAA  6  3  5  8    1
1  id_9    3  7  5  7  3  BBB
2  id_7    4  2  3  5  4    2
3  id_0    7  3  5  7  9    4
4  id_0    2  4  6  4  0    2

In [272]: df.dtypes
Out[272]:
id    object
a     object
b      int64
c      int64
d      int64
e      int64
f     object
dtype: object

Converting selected columns to numeric dtypes:

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

PS if you want to select all string (object) columns use the following simple trick:

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

Convert multiple columns to string in pandas dataframe

To convert multiple columns to string, include a list of columns to your above-mentioned command:

df[['one', 'two', 'three']] = df[['one', 'two', 'three']].astype(str)
# add as many column names as you like.

That means that one way to convert all columns is to construct the list of columns like this:

all_columns = list(df) # Creates list of all column headers
df[all_columns] = df[all_columns].astype(str)

Note that the latter can also be done directly (see comments).

change multiple columns in pandas dataframe to datetime

You can use apply to iterate through each column using pd.to_datetime

data.iloc[:, 7:12] = data.iloc[:, 7:12].apply(pd.to_datetime, errors='coerce')

As part of the changes in pandas 1.3.0, iloc/loc will no longer update the column dtype on assignment. Use column labels directly instead:

cols = data.columns[7:12]
data[cols] = data[cols].apply(pd.to_datetime, errors='coerce')

How to convert multiple columns from string to integer in pandas dataframe?

Try with replace():

df_all['1981'] = df_all['1981'].replace(',','',regex=True)

Now try with astype() method:

df_all['1981'] = df_all['1981'].astype('int64')

If you want to convert multiple columns then:

df[df.columns[2:]]=df[df.columns[2:]].replace(',','',regex=True).astype('int64')

changing data types of multiple columns at once in python/pandas

Another way would be to use astype in a for loop.

cat_cols = [col for col in df.columns if col not in ['col1', 'col5']]

for col in cat_cols:
    df[col] = df[col].astype('category')

How to convert multiple columns in one column in pandas?

Use melt:

>>> df.melt(var_name='route', value_name='edge')
    route   edge
0  route1   19.0
1  route1   47.0
2  route1   56.0
3  route1   43.0
4  route2   51.0
5  route2   46.0
6  route2   37.0
7  route2    2.0

If you have some columns to protect, use id_vars=['col1', 'col2', ...] to not flatten them.

How to convert datatype of multiple columns at once based on pattern

Iterating of pd.DataFrame.columns returns str, which does have endswiths but not contains, which is a method of pandas str accessor:

import pandas as pd

df = pd.DataFrame(columns=['a', 'btime', 'timec', 'timedtime', 'e'])
for c in df.columns:
    print(type(c), hasattr(c, 'endswith'), hasattr(c, 'contains'))

Output:

# type    endswith contains
<class 'str'> True False
...

Also, df.filter(like='time').columns:

Index(['btime', 'timec', 'timedtime'], dtype='object')

returns just as desired:

like : string
Keep labels from axis for which “like in label == True”.