In [1]: df
Out[1]:
data
0 1
1 2
2 3
3 4
You want to apply a function that conditionally returns a value based on the selected dataframe column.
In [2]: df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')
Out[2]:
0 true
1 true
2 false
3 false
Name: data
You can then assign that returned column to a new column in your dataframe:
In [3]: df['desired_output'] = df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')
In [4]: df
Out[4]:
data desired_output
0 1 true
1 2 true
2 3 false
3 4 false
GeeksforGeeks
geeksforgeeks.org › python › using-apply-in-pandas-lambda-functions-with-multiple-if-statements
Using Apply in Pandas Lambda functions with multiple if statements - GeeksforGeeks
June 20, 2025 - We create a simple DataFrame with student names and their marks. This will be used to classify students using custom logic. Lambda functions in apply() are ideal for simple conditions such as pass/fail classification. ... import pandas as pd df = pd.DataFrame({'Name': ['John', 'Jack', 'Shri', 'Krishna', 'Smith', 'Tessa'], 'Maths': [5, 3, 9, 10, 6, 3]}) # Adding the result column df['Result'] = df['Maths'].apply(lambda x: 'Pass' if x>=5 else 'Fail') print(df)
Top answer 1 of 5
77
In [1]: df
Out[1]:
data
0 1
1 2
2 3
3 4
You want to apply a function that conditionally returns a value based on the selected dataframe column.
In [2]: df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')
Out[2]:
0 true
1 true
2 false
3 false
Name: data
You can then assign that returned column to a new column in your dataframe:
In [3]: df['desired_output'] = df['data'].apply(lambda x: 'true' if x <= 2.5 else 'false')
In [4]: df
Out[4]:
data desired_output
0 1 true
1 2 true
2 3 false
3 4 false
2 of 5
32
Just compare the column with that value:
In [9]: df = pandas.DataFrame([1,2,3,4], columns=["data"])
In [10]: df
Out[10]:
data
0 1
1 2
2 3
3 4
In [11]: df["desired"] = df["data"] > 2.5
In [11]: df
Out[12]:
data desired
0 1 False
1 2 False
2 3 True
3 4 True
python - Using lambda if condition on different columns in Pandas dataframe - Stack Overflow
See similar questions with these tags. ... 0 Using lambda IF condition on columns in Pandas to extract and replace strings from a data frame comparing to a list More on stackoverflow.com
python - Using Apply in Pandas Lambda functions with multiple if statements - Stack Overflow
I'm trying to infer a classification according to the size of a person in a dataframe like this one: Size 1 80000 2 8000000 3 8000000000 ... I want it to look like this: S... More on stackoverflow.com
pandas - Python lambda function if else condition - Stack Overflow
Question: Trying to understand someone else's code. Can someone please explain what the lambda function is doing here?. Does the lambda function here translates to: If the first 3 digits of OrderNu... More on stackoverflow.com
Lambda if statement
You may be better just writing a normal function if it gets too complicated to understand, but for the sake of learning: something['SomeVariable'].apply(lambda x: dosomething() if isinstance(x, str) else dosomethingelse()) More on reddit.com
9
6
August 18, 2015Videos
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Data to Fish
datatofish.com › if-condition-in-pandas-dataframe
Two Ways to Apply an If-Condition on a pandas DataFrame
import pandas as pd data = {'fish': ['salmon', 'pufferfish', 'shark'], 'caught_count': [100, 5, 0] } df = pd.DataFrame(data) df['caught_count'] = df['fish'].apply(lambda x: 10 if x == "pufferfish") df['ge_100'] = df['caught_count'].apply(lambda x: True if x >= 100 else False)
Medium
medium.com › @whyamit101 › using-pandas-lambda-if-else-0d8368b70459
Using pandas lambda if else. The biggest lie in data science? That… | by why amit | Medium
April 12, 2025 - Can I use multiple conditions in a pandas lambda if else statement? Absolutely! You can nest if statements within your lambda function to handle multiple conditions. How do I apply a lambda function to an entire DataFrame?
Top answer 1 of 3
55
is that what you want?
In [300]: frame[['b','c']].apply(lambda x: x['c'] if x['c']>0 else x['b'], axis=1)
Out[300]:
0 -1.099891
1 0.582815
2 0.901591
3 0.900856
dtype: float64
2 of 3
8
Solution
use a vectorized approach
frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)
Explanation
This is derived from the sum of
(frame.c > 0) * frame.c # frame.c if positive
Plus
(frame.c <= 0) * frame.b # frame.b if c is not positive
However
(frame.c <=0 )
is equivalent to
(1 - frame.c > 0)
and when combined you get
frame['d'] = frame.b + (frame.c > 0) * (frame.c - frame.b)
ProjectPro
projectpro.io › blog › how to apply lambda functions to python pandas?
How To Apply Lambda Functions To Python Pandas?
October 28, 2024 - You can use Lambda functions in Pandas to apply conditional logic to data. This can be done by using nested if-else statements within the lambda function.
Statology
statology.org › home › pandas: how to use apply & lambda together
Pandas: How to Use Apply & Lambda Together
June 23, 2022 - You can use the following basic syntax to apply a lambda function to a pandas DataFrame: df['col'] = df['col'].apply(lambda x: 'value1' if x < 20 else 'value2') The following examples show how to use this syntax in practice with the following pandas DataFrame:
GeeksforGeeks
geeksforgeeks.org › python › ways-to-apply-an-if-condition-in-pandas-dataframe
How to apply if condition in Pandas DataFrame - GeeksforGeeks
July 15, 2025 - import pandas as pd # Sample DataFrame data = {'Name': ['John', 'Sophia', 'Daniel', 'Emma'], 'Experience': [5, 8, 3, 10]} df = pd.DataFrame(data) print("Original Dataset") display(df) # Apply if condition using lambda function df['Category'] = df['Experience'].apply(lambda x: 'Senior' if x >= 5 else 'Junior') print("Dataset with 'Senior'and 'Junior' Category") display(df)
Top answer 1 of 4
24
Here is a small example that you can build upon:
Basically, lambda x: x.. is the short one-liner of a function. What apply really asks for is a function which you can easily recreate yourself.
import pandas as pd
# Recreate the dataframe
data = dict(Size=[80000,8000000,800000000])
df = pd.DataFrame(data)
# Create a function that returns desired values
# You only need to check upper bound as the next elif-statement will catch the value
def func(x):
if x < 1e6:
return "<1m"
elif x < 1e7:
return "1-10m"
elif x < 5e7:
return "10-50m"
else:
return 'N/A'
# Add elif statements....
df['Classification'] = df['Size'].apply(func)
print(df)
Returns:
Size Classification
0 80000 <1m
1 8000000 1-10m
2 800000000 N/A
2 of 4
8
The apply lambda function actually does the job here, I just wonder what the problem was.... as your syntax looks ok and it works....
df1= [80000, 8000000, 8000000000, 800000000000]
df=pd.DataFrame(df1)
df.columns=['size']
df['Classification']=df['size'].apply(lambda x: '<1m' if x<1000000 else '1-10m' if 1000000<x<10000000 else '1bi')
df
Output:
Towards Data Science
towardsdatascience.com › home › latest › 5 ways to apply if-else conditional statements in pandas
5 Ways to Apply If-Else Conditional Statements in Pandas | Towards Data Science
January 28, 2025 - df['new column name'] = df['column name'].apply(lambda x: 'value if condition is true' if x condition else 'value if condition is false') For our specific example, the code would look like this: df['visits_category'] = df['visits_30days'].a...
Stack Overflow
stackoverflow.com › questions › 71260517 › python-lambda-function-if-else-condition › 71260558
pandas - Python lambda function if else condition - Stack Overflow
Does the lambda function here translates to: If the first 3 digits of OrderNumber are not 486 and not 561, and the first digit is not 8 then set the column value data_df[OrderNumber] of the dataframe to empty string; otherwise leave it as it is? import sqlalchemy as sq import pandas as pd data_df = pd.read_csv('/dbfs/FileStore/tables/CustomerOrders.txt', sep=',', low_memory=False, quotechar='"', header='infer' , encoding='cp1252') data_df[OrderNumber] = data_df[OrderNumber].apply(lambda x: x if x[:3] != '486' and x[:3] != '561' and x[:1] != '8' else "") .............
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.apply.html
pandas.DataFrame.apply — pandas 3.0.1 documentation
If “compat”, will if possible first translate the func into pandas methods (e.g. Series().apply(np.sum) will be translated to Series().sum()). If that doesn’t work, will try call to apply again with by_row=True and if that fails, will call apply again with by_row=False (backward compatible).
Reddit
reddit.com › r/learnpython › lambda if statement
r/learnpython on Reddit: Lambda if statement
August 18, 2015 -
Hello,
I'm trying to do a lambda statement where I check the type of the value, how do I implement if in lambda?
something['SomeVariable'].apply(lambda x: <if isinstance(x, str) = true, dosomething>)
Top answer 1 of 3
5
You may be better just writing a normal function if it gets too complicated to understand, but for the sake of learning: something['SomeVariable'].apply(lambda x: dosomething() if isinstance(x, str) else dosomethingelse())
2 of 3
2
You need to use an if expression, which is one line and contains an else. So like this: Whatever.apply(lambda x: domsomething(x) if isinstance(x, str) else None) lambda and def are similar with two exceptions: With def you must provide a name, with lambda this is optional and must be done like something = lambda: ... lambda is restricted to a single expression and its result is the return value. Python doesn't have "true" anonymous functions like JavaScript and PHP, and I doubt we'll ever get them.
Delft Stack
delftstack.com › home › howto › python pandas › apply lambda functions to pandas dataframe
How to Apply Lambda Function to Pandas DataFrame | Delft Stack
February 2, 2024 - We can also apply the conditional statements on pandas dataframes using the lambda function. We used the conditional statement inside the lambda function in the following example. We applied the condition on the Monthly Income column. If the monthly income is greater and equal to 5000, add Stable inside the Category column; otherwise, add UnStable.
Top answer 1 of 4
224
Nest if .. elses:
lambda x: x*10 if x<2 else (x**2 if x<4 else x+10)
2 of 4
50
I do not recommend the use of apply here: it should be avoided if there are better alternatives.
For example, if you are performing the following operation on a Series:
if cond1:
exp1
elif cond2:
exp2
else:
exp3
This is usually a good use case for np.where or np.select.
numpy.where
The if else chain above can be written using
np.where(cond1, exp1, np.where(cond2, exp2, ...))
np.where allows nesting. With one level of nesting, your problem can be solved with,
df['three'] = (
np.where(
df['one'] < 2,
df['one'] * 10,
np.where(df['one'] < 4, df['one'] ** 2, df['one'] + 10))
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
numpy.select
Allows for flexible syntax and is easily extensible. It follows the form,
np.select([cond1, cond2, ...], [exp1, exp2, ...])
Or, in this case,
np.select([cond1, cond2], [exp1, exp2], default=exp3)
df['three'] = (
np.select(
condlist=[df['one'] < 2, df['one'] < 4],
choicelist=[df['one'] * 10, df['one'] ** 2],
default=df['one'] + 10))
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
and/or (similar to the if/else)
Similar to if-else, requires the lambda:
df['three'] = df["one"].apply(
lambda x: (x < 2 and x * 10) or (x < 4 and x ** 2) or x + 10)
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
List Comprehension
Loopy solution that is still faster than apply.
df['three'] = [x*10 if x<2 else (x**2 if x<4 else x+10) for x in df['one']]
# df['three'] = [
# (x < 2 and x * 10) or (x < 4 and x ** 2) or x + 10) for x in df['one']
# ]
df
one two three
0 1 6 10
1 2 7 4
2 3 8 9
3 4 9 14
4 5 10 15
CSDN
devpress.csdn.net › python › 63046073c67703293080c104.html
Using Apply in Pandas Lambda functions with multiple if statements_python_Mangs-Python
df['Classification']=df['Size'].apply(lambda x: "<1m" if x<1000000 else "1-10m" if 1000000<x<10000000 else ...)
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas apply() with lambda examples
Pandas apply() with Lambda Examples - Spark By {Examples}
June 17, 2025 - A lambda function is a small anonymous function that can take any number of arguments and execute an expression. ... In this article, I will explain how to use a Pandas DataFrame.apply() with lambda by examples. lambda expressions are utilized to construct anonymous functions.
Top answer 1 of 3
1
Alternatively, you can use loc:
import pandas as pd
df = pd.DataFrame({"age": [-100, 300, 400, 500, 600, 700]})
df["age"].loc[(df["age"] < 500) & (df["age"] >= 0)] = 0
Now your df looks like this:
age
0 -100
1 0
2 0
3 500
4 600
5 700
2 of 3
0
You can use Nested List comprehension within the lambda function.
Or
Write a function and call the function on your series using Lambda
Saturn Cloud
saturncloud.io › blog › how-to-use-ifelse-function-in-pandas-dataframe
How to Use If-Else Function in Pandas DataFrame | Saturn Cloud Blog
November 10, 2023 - To use the if-else function in Pandas DataFrame, you can use the apply() function along with a lambda function. The apply() function applies a function along an axis of the DataFrame.
Analytics Vidhya
analyticsvidhya.com › home › learn how to use lambda functions in python easily and effectively
Learn How to Use Lambda Functions in Python Easily and Effectively
December 1, 2024 - We can do this with the apply() ... a dataframe. If the axis argument in the apply() function is 0, then the lambda function gets applied to each column, and if 1, then the function gets applied to each row....