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How can we apply a function to a column in Pandas Dataframe in the fastest and most efficient way possible? These guidelines are your immediate solutions. Check them out for tips, examples, and our take on some other common issues!
How to Apply a Function to a Column in Pandas Dataframe
First, we will create a referential DataFrame, which we will refer to in our article to help you understand some core concepts.
Example (DataFrame)
import pandas as pd
df = pd.DataFrame({
'colA': [1, 2, 3, 4, 5],
'colB': [True, False, None, False, True],
'colC': ['a', 'b', 'c', 'd', 'e'],
'colD': [1.0, 2.0, 3.0, 4.0, 5.0]
})
print(df)
colA colB colC colD
0 1 True a 1.0
1 2 False b 2.0
2 3 None c 3.0
3 4 False d 4.0
4 5 True e 5.0
Also, we will assume you wish to add “1” to each value beneath the column “colA”. Lambda expressions could easily do that for you.
Example (Code):
lambda x: x + 1
Method 1: Apply A Function to One Column Only
If you only wish to apply functions to one column, the fastest method is to employ the map().
Example (Solution 1):
df['colA'] = df['colA'].map(lambda x: x + 1)
print(df)
colA colB colC colD
0 2 True a 1.0
1 3 False b 2.0
2 4 None c 3.0
3 5 False d 4.0
4 6 True e 5.0
The apply()
is also a great solution. Still, map()
will work better for single-column applications.
The Pandas.series.map() approach runs on Series (DataFrames’ single columns) and cells at one time. Meanwhile, pandas.DataFrame.apply() runs on the entire row at one time. Hence, map() is more effective since there is only the need to gain access to one particular column’s values (instead of one complete row).
Method 2: Apply A Function to Several Columns
On another note, if you wish to apply one specific function to several columns at the same time, pandas.DataFrame.apply() will be a great choice. Let’s say you want to insert the “lambda” function (lambda x: x + 1) on colA and colD. The codes below will help you with that:
Example (Solution 2):
df[['colA', 'colD']] = df[['colA', 'colD']].apply(lambda x: x + 1)
print(df)
colA colB colC colD
0 2 True a 2.0
1 3 False b 3.0
2 4 None c 4.0
3 5 False d 5.0
4 6 True e 6.0
In this example, we should not (and cannot) use the map () function. That is because the map () function can only run over objects from Series (such as single columns, like in Method 1). Meanwhile, our apply() function can run one specific lambda command over an entire row, which, in this example, comprises colA and colD’s values.
FAQs
1. Is It Possible for A Pandas Column to Have Mixed Types?
Yes, you can. When running your program on “read_csv”, the warnings that your Pandas columns have mixed types will sometimes pop up. For instance: 1,4,d,g,e,2,3,a is a mixture of integers and strings. Still, instead of forcing you to stop the program, Pandas will try to guess what dtype that column has, which is great!
2. Can I Know Whether The Two Columns Are Identical? And If Yes, What Should I Do?
Of course, there are solutions to identify whether your columns are the same. One tactic is to adopt the Equals() functions.
- The syntax: DataFrame.equals(other)
- The parameters: DataFrame or OtherSeries, which means your DataFrame or other Series will be pitted against the first for comparison.
- The returns: If both objects have the same elements, the returns will be “True”. Otherwise, they are “False”.
Conclusion
Our article has given you two great methods to apply a function to a column in Pandas Dataframe. Some other common inquiries have also been tackled. We hope that these guidelines will prove useful to you! On tips to select Pandas columns by index or by name, feel free to browse our website for more instructions.
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