How to add multiple columns to pandas dataframe in one assignment?
PythonPandasDataframePython Problem Overview
I'm new to pandas and trying to figure out how to add multiple columns to pandas simultaneously. Any help here is appreciated. Ideally I would like to do this in one step rather than multiple repeated steps...
import pandas as pd
df = {'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)
df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3] #thought this would work here...
Python Solutions
Solution 1 - Python
I would have expected your syntax to work too. The problem arises because when you create new columns with the column-list syntax (df[[new1, new2]] = ...), pandas requires that the right hand side be a DataFrame (note that it doesn't actually matter if the columns of the DataFrame have the same names as the columns you are creating).
Your syntax works fine for assigning scalar values to existing columns, and pandas is also happy to assign scalar values to a new column using the single-column syntax (df[new1] = ...). So the solution is either to convert this into several single-column assignments, or create a suitable DataFrame for the right-hand side.
Here are several approaches that will work:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
Then one of the following:
1) Three assignments in one, using list unpacking:
df['column_new_1'], df['column_new_2'], df['column_new_3'] = [np.nan, 'dogs', 3]
2) DataFrame conveniently expands a single row to match the index, so you can do this:
df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)
3) Make a temporary data frame with new columns, then combine with the original data frame later:
df = pd.concat(
[
df,
pd.DataFrame(
[[np.nan, 'dogs', 3]],
index=df.index,
columns=['column_new_1', 'column_new_2', 'column_new_3']
)
], axis=1
)
4) Similar to the previous, but using join instead of concat (may be less efficient):
df = df.join(pd.DataFrame(
[[np.nan, 'dogs', 3]],
index=df.index,
columns=['column_new_1', 'column_new_2', 'column_new_3']
))
5) Using a dict is a more "natural" way to create the new data frame than the previous two, but the new columns will be sorted alphabetically (at least before Python 3.6 or 3.7):
df = df.join(pd.DataFrame(
{
'column_new_1': np.nan,
'column_new_2': 'dogs',
'column_new_3': 3
}, index=df.index
))
6) Use .assign() with multiple column arguments.
I like this variant on @zero's answer a lot, but like the previous one, the new columns will always be sorted alphabetically, at least with early versions of Python:
df = df.assign(column_new_1=np.nan, column_new_2='dogs', column_new_3=3)
7) This is interesting (based on https://stackoverflow.com/a/44951376/3830997), but I don't know when it would be worth the trouble:
new_cols = ['column_new_1', 'column_new_2', 'column_new_3']
new_vals = [np.nan, 'dogs', 3]
df = df.reindex(columns=df.columns.tolist() + new_cols) # add empty cols
df[new_cols] = new_vals # multi-column assignment works for existing cols
8) In the end it's hard to beat three separate assignments:
df['column_new_1'] = np.nan
df['column_new_2'] = 'dogs'
df['column_new_3'] = 3
Note: many of these options have already been covered in other answers: https://stackoverflow.com/q/43415798/3830997, https://stackoverflow.com/q/19866377/3830997, https://stackoverflow.com/q/30926670/3830997
Solution 2 - Python
You could use assign with a dict of column names and values.
In [1069]: df.assign(**{'col_new_1': np.nan, 'col2_new_2': 'dogs', 'col3_new_3': 3})
Out[1069]:
col_1 col_2 col2_new_2 col3_new_3 col_new_1
0 0 4 dogs 3 NaN
1 1 5 dogs 3 NaN
2 2 6 dogs 3 NaN
3 3 7 dogs 3 NaN
Solution 3 - Python
With the use of concat:
In [128]: df
Out[128]:
col_1 col_2
0 0 4
1 1 5
2 2 6
3 3 7
In [129]: pd.concat([df, pd.DataFrame(columns = [ 'column_new_1', 'column_new_2','column_new_3'])])
Out[129]:
col_1 col_2 column_new_1 column_new_2 column_new_3
0 0.0 4.0 NaN NaN NaN
1 1.0 5.0 NaN NaN NaN
2 2.0 6.0 NaN NaN NaN
3 3.0 7.0 NaN NaN NaN
Not very sure of what you wanted to do with [np.nan, 'dogs',3]. Maybe now set them as default values?
In [142]: df1 = pd.concat([df, pd.DataFrame(columns = [ 'column_new_1', 'column_new_2','column_new_3'])])
In [143]: df1[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs', 3]
In [144]: df1
Out[144]:
col_1 col_2 column_new_1 column_new_2 column_new_3
0 0.0 4.0 NaN dogs 3
1 1.0 5.0 NaN dogs 3
2 2.0 6.0 NaN dogs 3
3 3.0 7.0 NaN dogs 3
Solution 4 - Python
use of list comprehension, pd.DataFrame and pd.concat
pd.concat(
[ df, pd.DataFrame( [[np.nan, 'dogs', 3] for _ in range(df.shape[0])],
df.index, ['column_new_1', 'column_new_2','column_new_3']
)
], axis=1)
Solution 5 - Python
if adding a lot of missing columns (a, b, c ,....) with the same value, here 0, i did this:
new_cols = ["a", "b", "c" ]
df[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=df.index)
It's based on the second variant of the accepted answer.
Solution 6 - Python
Just want to point out that option2 in @Matthias Fripp's answer >(2) I wouldn't necessarily expect DataFrame to work this way, but it does > >df[['column_new_1', 'column_new_2', 'column_new_3']] = pd.DataFrame([[np.nan, 'dogs', 3]], index=df.index)
is already documented in pandas' own documentation http://pandas.pydata.org/pandas-docs/stable/indexing.html#basics > You can pass a list of columns to [] to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner. You may find this useful for applying a transform (in-place) to a subset of the columns.
Solution 7 - Python
Dictionary mapping with .assign():
This is the most readable and dynamic way to assign new column(s) with value(s) when working with many of them.
import pandas as pd
import numpy as np
new_cols = ["column_new_1", "column_new_2", "column_new_3"]
new_vals = [np.nan, "dogs", 3]
# Map new columns as keys and new values as values
col_val_mapping = dict(zip(new_cols, new_vals))
# Unpack new column/new value pairs and assign them to the data frame
df = df.assign(**col_val_mapping)
If you're just trying to initialize the new column values to be empty as you either don't know what the values are going to be or you have many new columns.
import pandas as pd
import numpy as np
new_cols = ["column_new_1", "column_new_2", "column_new_3"]
new_vals = [None for item in new_cols]
# Map new columns as keys and new values as values
col_val_mapping = dict(zip(new_cols, new_vals))
# Unpack new column/new value pairs and assign them to the data frame
df = df.assign(**col_val_mapping)
Solution 8 - Python
If you just want to add empty new columns, reindex will do the job
df
col_1 col_2
0 0 4
1 1 5
2 2 6
3 3 7
df.reindex(list(df)+['column_new_1', 'column_new_2','column_new_3'], axis=1)
col_1 col_2 column_new_1 column_new_2 column_new_3
0 0 4 NaN NaN NaN
1 1 5 NaN NaN NaN
2 2 6 NaN NaN NaN
3 3 7 NaN NaN NaN
full code example
import numpy as np
import pandas as pd
df = {'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)
print('df',df, sep='\n')
print()
df=df.reindex(list(df)+['column_new_1', 'column_new_2','column_new_3'], axis=1)
print('''df.reindex(list(df)+['column_new_1', 'column_new_2','column_new_3'], axis=1)''',df, sep='\n')
Solution 9 - Python
I am not comfortable using "Index" and so on...could come up as below
df.columns
Index(['A123', 'B123'], dtype='object')
df=pd.concat([df,pd.DataFrame(columns=list('CDE'))])
df.rename(columns={
'C':'C123',
'D':'D123',
'E':'E123'
},inplace=True)
df.columns
Index(['A123', 'B123', 'C123', 'D123', 'E123'], dtype='object')
Solution 10 - Python
You could instantiate the values from a dictionary if you wanted different values for each column & you don't mind making a dictionary on the line before.
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
>>> df
col_1 col_2
0 0 4
1 1 5
2 2 6
3 3 7
>>> cols = {
'column_new_1':np.nan,
'column_new_2':'dogs',
'column_new_3': 3
}
>>> df[list(cols)] = pd.DataFrame(data={k:[v]*len(df) for k,v in cols.items()})
>>> df
col_1 col_2 column_new_1 column_new_2 column_new_3
0 0 4 NaN dogs 3
1 1 5 NaN dogs 3
2 2 6 NaN dogs 3
3 3 7 NaN dogs 3
Not necessarily better than the accepted answer, but it's another approach not yet listed.
Solution 11 - Python
import pandas as pd
df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
df['col_3'], df['col_4'] = [df.col_1]*2
>> df
col_1 col_2 col_3 col_4
0 4 0 0
1 5 1 1
2 6 2 2
3 7 3 3