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5 ways to apply an IF condition in Pandas DataFrame

In this guide, you’ll see 5 different ways to apply an IF condition in Pandas DataFrame.

Specifically, you’ll see how to apply an IF condition for:

  • Set of numbers
  • Set of numbers and lambda
  • Strings and lambda
  • OR condition

Applying an IF condition in Pandas DataFrame

Let’s now review the following 5 cases:

(1) IF condition – Set of numbers

Suppose that you created a DataFrame in Python that has 10 numbers (from 1 to 10). You then want to apply the following IF conditions:

  • If the number is equal or lower than 4, then assign the value of ‘True’
  • Otherwise, if the number is greater than 4, then assign the value of ‘False’

This is the general structure that you may use to create the IF condition:

For our example, the Python code would look like this:

Here is the result that you’ll get in Python:

(2) IF condition – set of numbers and  lambda

You’ll now see how to get the same results as in case 1 by using lambda, where the conditions are:

Here is the generic structure that you may apply in Python:

And for our example:

This is the result that you’ll get, which matches with case 1:

(3) IF condition – strings

Now, let’s create a DataFrame that contains only strings/text with 4  names : Jon, Bill, Maria and Emma.

The conditions are:

  • If the name is equal to ‘Bill,’ then assign the value of ‘Match’
  • Otherwise, if the name is not  ‘Bill,’ then assign the value of ‘Mismatch’

Once you run the above Python code, you’ll see:

(4) IF condition – strings and lambda 

You’ll get the same results as in case 3 by using lambda:

And here is the output from Python:

(5) IF condition with OR

Now let’s apply these conditions:

  • If the name is ‘Bill’  or ‘Emma,’ then assign the value of ‘Match’
  • Otherwise, if the name is neither ‘Bill’ nor ‘Emma,’ then assign the value of ‘Mismatch’

Run the Python code, and you’ll get the following result:

Applying an IF condition under an existing DataFrame column

So far you have seen how to apply an IF condition by creating a new column.

Alternatively, you may store the results under an existing DataFrame column.

For example, let’s say that you created a DataFrame that has 12 numbers, where the last two numbers are zeros:

‘set_of_numbers’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0 , 0 ]

You may then apply the following IF conditions, and then store the results under the existing ‘set_of_numbers’ column:

  • If the number is equal to 0, then change the value to 999
  • If the number is equal to 5, then change the value to 555

Here are the before and after results, where the ‘5’ became ‘555’ and the 0’s became ‘999’ under the existing ‘set_of_numbers’ column:

On another instance, you may have a DataFrame that contains NaN values . You can then apply an IF condition to replace those values with zeros , as in the example below:

Before you’ll see the NaN values, and after you’ll see the zero values:

You just saw how to apply an IF condition in Pandas DataFrame . There are indeed multiple ways to apply such a condition in Python. You can achieve the same results by using either lambda, or just by sticking with Pandas.

At the end, it boils down to working with the method that is best suited to your needs.

Finally, you may want to check the following external source for additional information about Pandas DataFrame .

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Set Pandas Conditional Column Based on Values of Another Column

  • August 9, 2021 February 22, 2022

Learn how to create a pandas conditional column cover image

There are many times when you may need to set a Pandas column value based on the condition of another column. In this post, you’ll learn all the different ways in which you can create Pandas conditional columns.

Table of Contents

Video Tutorial

If you prefer to follow along with a video tutorial, check out my video below:

Loading a Sample Dataframe

Let’s begin by loading a sample Pandas dataframe that we can use throughout this tutorial.

We’ll begin by import pandas and loading a dataframe using the .from_dict() method:

This returns the following dataframe:

Using Pandas loc to Set Pandas Conditional Column

Pandas loc is incredibly powerful! If you need a refresher on loc (or iloc), check out my tutorial here . Pandas’ loc creates a boolean mask, based on a condition. Sometimes, that condition can just be selecting rows and columns, but it can also be used to filter dataframes. These filtered dataframes can then have values applied to them.

Let’s explore the syntax a little bit:

With the syntax above, we filter the dataframe using .loc and then assign a value to any row in the column (or columns) where the condition is met.

Let’s try this out by assigning the string ‘Under 30’ to anyone with an age less than 30, and ‘Over 30’ to anyone 30 or older.

Let's take a look at what we did here:

  • We assigned the string 'Over 30' to every record in the dataframe. To learn more about this, check out my post here or creating new columns.
  • We then use .loc to create a boolean mask on the Age column to filter down to rows where the age is less than 30. When this condition is met, the Age Category column is assigned the new value 'Under 30'

But what happens when you have multiple conditions? You could, of course, use .loc multiple times, but this is difficult to read and fairly unpleasant to write. Let's see how we can accomplish this using numpy's .select() method.

Using Numpy Select to Set Values using Multiple Conditions

Similar to the method above to use .loc to create a conditional column in Pandas, we can use the numpy .select() method.

Let's begin by importing numpy and we'll give it the conventional alias np :

Now, say we wanted to apply a number of different age groups, as below:

  • <20 years old,
  • 20-39 years old,
  • 40-59 years old,
  • 60+ years old

In order to do this, we'll create a list of conditions and corresponding values to fill:

Running this returns the following dataframe:

Let's break down what happens here:

  • We first define a list of conditions in which the criteria are specified. Recall that lists are ordered meaning that they should be in the order in which you would like the corresponding values to appear.
  • We then define a list of values to use , which corresponds to the values you'd like applied in your new column.

Something to consider here is that this can be a bit counterintuitive to write. You can similarly define a function to apply different values. We'll cover this off in the section of using the Pandas .apply() method below .

One of the key benefits is that using numpy as is very fast, especially when compared to using the .apply() method.

Using Pandas Map to Set Values in Another Column

The Pandas .map() method is very helpful when you're applying labels to another column. In order to use this method, you define a dictionary to apply to the column.

For our sample dataframe, let's imagine that we have offices in America, Canada, and France. We want to map the cities to their corresponding countries and apply and "Other" value for any other city.

When we print this out, we get the following dataframe returned:

What we can see here, is that there is a NaN value associated with any City that doesn't have a corresponding country. If we want to apply "Other" to any missing values, we can chain the .fillna() method:

Using Pandas Apply to Apply a function to a column

Finally, you can apply built-in or custom functions to a dataframe using the Pandas .apply() method.

Let's take a look at both applying built-in functions such as len() and even applying custom functions.

Applying Python Built-in Functions to a Column

We can easily apply a built-in function using the .apply() method. Let's see how we can use the len() function to count how long a string of a given column.

Take note of a few things here:

  • We apply the .apply() method to a particular column,
  • We omit the parentheses "()"

Using Third-Party Packages in Pandas Apply

Similarly, you can use functions from using packages. Let's use numpy to apply the .sqrt() method to find the scare root of a person's age.

Using Custom Functions with Pandas Apply

Something that makes the .apply() method extremely powerful is the ability to define and apply your own functions.

Let's revisit how we could use an if-else statement to create age categories as in our earlier example:

In this post, you learned a number of ways in which you can apply values to a dataframe column to create a Pandas conditional column, including using .loc , .np.select() , Pandas .map() and Pandas .apply() . Each of these methods has a different use case that we explored throughout this post.

Learn more about Pandas methods covered here by checking out their official documentation:

  • Pandas Apply
  • Numpy Select

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

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How to Apply the If-Else Condition in a Pandas DataFrame

  • Python Pandas Howtos
  • How to Apply the If-Else Condition in a …

Use DataFrame.loc[] to Apply the if-else Condition in a Pandas DataFrame in Python

Use dataframe.apply() to apply the if-else condition in a pandas dataframe in python, use numpy.select() to apply the if-else condition in a pandas dataframe in python, use lambda with apply() to apply the if-else condition in a pandas dataframe in python.

How to Apply the If-Else Condition in a Pandas DataFrame

Pandas is an open-source data analysis library in Python. It provides many built-in methods to perform operations on numerical data.

In some cases, we want to apply the if-else conditions on a Pandas dataframe to filter the records or perform computations according to some conditions. Python provides many ways to use if-else on a Pandas dataframe.

loc[] is a property of the Pandas data frame used to select or filter a group of rows or columns. In the following example, we will employ this property to filter the records that meet a given condition.

Here, we have a Pandas data frame consisting of the students’ data. Using loc[] , we can only apply a single condition at a time.

We will filter those students having marks greater than or equal to 60 in the first condition and assign their result as Pass in the new column Result . Similarly, we will set Fail for the rest of the student’s results in another condition.

Example Code:

Pandas if else Using DataFrame.loc - Output

The apply() method uses the data frame’s axis (row or column) to apply a function. We can make our defined function that consists of if-else conditions and apply it to the Pandas dataframe.

Here, we have defined a function assign_Result() and applied it to the Marks column. The function consists of if-else conditions that assign the result based on the Marks and invoke this for every column row.

Pandas if else Using DataFrame.apply() - Output

We can define multiple conditions for a column in a list and their corresponding values in another list if the condition is True . The select() method takes the list of conditions and their corresponding list of values as arguments and assigns them to the Result column.

Pandas if else Using NumPy.select() - Output

A lambda is a small anonymous function consisting of a single expression. We will use lambda with apply() on the Marks column.

The x contains the marks in the lambda expression. We applied the if-else condition to the x and assigned the result accordingly in the Result column.

Pandas if else Using lambda With apply() - Output

I am Fariba Laiq from Pakistan. An android app developer, technical content writer, and coding instructor. Writing has always been one of my passions. I love to learn, implement and convey my knowledge to others.

Related Article - Pandas Condition

  • How to Create DataFrame Column Based on Given Condition in Pandas

Using If-Else Statements in Pandas: A Practical Guide [+ Examples]

Stephen Roddewig

Updated: March 21, 2022

Published: March 14, 2022

One of the core benefits of programming is automation. Instead of doing something manually, you issue instructions to a computer that executes the task for you.

Data engineer writing an if-else statement in pandas

To ensure the program returns the expected result, you need to provide explicit guide rails that instruct the computer how to respond in various scenarios. In programming, this concept is known as control flow. A major piece of control flow is defining computer logic, and one of the fundamental methods for providing this framework for a program is the if-else statement.

pandas is a Python library built to work with relational data at scale. As you work with values captured in pandas Series and DataFrames, you can use if-else statements and their logical structure to categorize and manipulate your data to reveal new insights.

Let's break down how to use if-else statements in pandas, starting with how to define the statements themselves.

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Pandas If Else Statement

Before diving into how to use if-else statements in pandas, let's break down the basic syntax.

In this example, you have a Series of test scores and want to know how many values are above the passing benchmark. You can inspect the Series below.

Series containing test scores ranging from 67 to 96

The output is below.

Number of passing tests showing 7 printed to the terminal

Now, say you want to take the numerical test scores and find their letter grade equivalents. Like before, you are interested in only aggregate counts versus the individual scores.

In this example, you define a dictionary to hold the counts of each grade as a property under one variable: letter_grades . You then update the for loop with elif statements:

letter_grades = {

    'a_count': 0,

    'b_count': 0,

    'c_count': 0,

    'd_count': 0,

    'f_count': 0

    if grade >= 90:

        letter_grades['a_count'] += 1

    elif grade >= 80:

        letter_grades['b_count'] += 1

    elif grade >= 70:

        letter_grades['c_count'] += 1

    elif grade >= 60:

        letter_grades['d_count'] += 1

        letter_grades['f_count'] += 1

You are still using an if statement at the start and an else statement at the end. But now you have three elif statements between them to account for additional outcomes. A test score could evaluate as an A, B, C, D, or F, so the binary if-else statement from the last example would not suffice.

The for loop will move through each statement and stop at the first condition that evaluates to true. It will not execute any following statements after the first true condition, so a grade that evaluates to an A will not also increase the count of the other properties. If none of the conditions return true, it executes the else statement.

The outcome of executing the expanded for loop is below.

Counts of different letter grades printed to the terminal

Borrowing the logic defined in the last example, you can apply a custom function that returns the letter grade that corresponds to each numerical test score by calling .apply() :

def assign_letter(row):

    if row >= 90:

        result = 'a'

    elif row >= 80:

        result = 'b'

    elif row >= 70:

        result = 'c'

    elif row >= 60:

        result = 'd'

        result = 'f'

    return result

grades_df['letter_grades'] = grades_df['grades'].apply(assign_letter)

First, you declare a function with the def keyword and assign the function a name (assign_letter) so you can pass it as an argument in .apply() . assign_letter() takes one argument (row), which is a placeholder for the values that will be passed in for each row in the DataFrame.

Within assign_letter() , you have an if-else statement that evaluates the row values. Whenever a condition is met, the temporary variable result is declared that stores the letter grade as a string. Since the if-else statement stops execution once one statement evaluates to true or else is reached, result is immediately returned, and .apply() moves to the next row.

. apply() runs the assign_letter() function against each row and compiles a Series of the results. In this case, the indexing operator ([ ]) is used to specify that . apply() only targets the values contained under the "grades" column versus the full rows of the grades_df DataFrame. Otherwise, assign_letter() will attempt to evaluate whether the student name strings are greater than or equal to the integers you provided, resulting in a TypeError.

The result of calling .apply is below.

DataFrame with new column name "letter_grades" and letter grades corresponding to test scores printed to the terminal

2. The .loc Method

Best for : quickly defining simple logical statements in a few lines

The .loc method is an indexing operator that you can use to quickly evaluate row values when the condition has a binary outcome (either it is true or it is false).

For this example, the DataFrame holds numerical test scores for students, and you want to evaluate whose tests passed.

DataFrame showing students' names and corresponding test scores printed to the terminal

DataFrame with column name "passing" showing False for every student printed to the DataFrame

DataFrame showing two False values and six True values printed to the screen

Next, you declare another list to hold the values each condition will correspond to, in this case the letter grade strings:

letters = ['f', 'd', 'c', 'b', 'a']

Note that you need to match the order of the values to the order of conditions. Otherwise, scores below 60 would be marked as "a" and so on.

Now that you have declared both arguments, you're ready to call .select() :

grades_df['letter_grades'] = np.select(conditions, letters)

Here, you are creating a new column with the label "letter_grades" and setting it equal to the result of calling .select() from the NumPy (np) library. The method takes the conditions and letters lists as arguments and returns a list of results based on evaluating each row under the "grades" column.

You can confirm .select performed as expected by printing the DataFrame to the terminal:

DataFrame with new column name "letter_grades" and letter grades corresponding to test scores printed to the terminal

Use if-else statements in Pandas to find the answer faster.

If-else statements are a fundamental component of control flow in programming. When it comes to data analysis in pandas, they offer a convenient way to segment the data and produce new insights. Python, combined with its pandas and NumPy libraries, offers several strategies to incorporate if-else statements and their underlying logic into your analysis to better understand your data and apply it to your most pressing business challenges.

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Pandas DataFrame Practice Exercises

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  • Reset Index in Pandas Dataframe
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  • How to print an entire Pandas DataFrame in Python?
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Pandas Dataframe Rows Practice Exercise

  • How to iterate over rows in Pandas Dataframe
  • Different ways to iterate over rows in Pandas Dataframe
  • Selecting rows in pandas DataFrame based on conditions
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Pandas Dataframe Columns Practice Exercise

  • Create a pandas column using for loop
  • How to get column names in Pandas dataframe
  • How to rename columns in Pandas DataFrame
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Conditional operation on Pandas DataFrame columns

  • Return the Index label if some condition is satisfied over a column in Pandas Dataframe
  • Using dictionary to remap values in Pandas DataFrame columns
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  • Getting Unique values from a column in Pandas dataframe
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  • Difference of two columns in Pandas dataframe
  • Get the index of maximum value in DataFrame column
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  • How to drop one or multiple columns in Pandas Dataframe
  • How to lowercase strings in a column in Pandas dataframe
  • Capitalize first letter of a column in Pandas dataframe
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Pandas Series Practice Exercise

  • Create a Pandas Series from array
  • Creating a Pandas Series from Dictionary
  • Creating a Pandas Series from Lists
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  • Access the elements of a Series in Pandas

Pandas Date and Time Practice Exercise

  • Basic of Time Series Manipulation Using Pandas
  • Using Timedelta and Period to create DateTime based indexes in Pandas
  • Convert the column type from string to datetime format in Pandas dataframe

DataFrame String Manipulation

  • Extract punctuation from the specified column of Dataframe using Regex
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Accessing and Manipulating Data in DataFrame

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DataFrame Visualization and Exporting

  • How to render Pandas DataFrame as HTML Table?
  • Exporting Pandas DataFrame to JSON File
  • Create and display a one-dimensional array-like object using Pandas in Python
  • Export Pandas dataframe to a CSV file
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Data Aggregation and Grouping

  • How to sum negative and positive values using GroupBy in Pandas?
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Merging and Joining

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  • Merge two Pandas dataframes by matched ID number
  • Merge two dataframes with same column names

Filtering and Selecting Data

  • Drop specific rows from multiindex Pandas Dataframe
  • Select rows that contain specific text using Pandas

Select Rows With Multiple Filters in Pandas

  • Select Pandas dataframe rows between two dates
  • Filter Pandas Dataframe with multiple conditions

Selection and Slicing

  • How to take column-slices of DataFrame in Pandas?
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  • How to reverse the column order of the Pandas DataFrame?
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Miscellaneous DataFrame Operations

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Data Cleaning and Manipulation

  • How to fill NAN values with mean in Pandas?
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  • How to remove random symbols in a dataframe in Pandas?
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  • Align columns to Left in Pandas - Python

Concatenation and Manipulation

  • Read multiple CSV files into separate DataFrames in Python
  • Append list of dictionary and series to a existing Pandas DataFrame in Python
  • Concatenate multiIndex into single index in Pandas Series
  • Concatenate strings from several rows using Pandas groupby
  • Split large Pandas Dataframe into list of smaller Dataframes

DataFrame Sorting and Reordering

  • How to Sort a Pandas DataFrame by Date?
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DataFrame Transformation and Conversion

  • Get the first 3 rows of a given DataFrame
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  • How to convert index in a column of the Pandas dataframe?
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DataFrame Filtering and Selection

  • Select a single column of data as a Series in Pandas
  • How to Select single column of a Pandas Dataframe?
  • Ways to filter Pandas DataFrame by column values
  • How to Filter DataFrame Rows Based on the Date in Pandas?

DataFrame Conversion and Reshaping

  • Convert a series of date strings to a time series in Pandas Dataframe
  • Split Pandas Dataframe by Rows
  • How to convert a dictionary to a Pandas series?
  • Flatten a list of DataFrames
  • Convert birth date to age in Pandas

Suppose you have an online store. The price of the products is updated frequently. While calculating the final price on the product, you check if the updated price is available or not. If not available then you use the last price available. Solution #1: We can use conditional expression to check if the column is present or not. If it is not present then we calculate the price using the alternative column. 

python pandas conditional assignment

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  • Vectorize conditional assignment in pandas dataframe

Given a pandas dataframe, we have to vectorize conditional assignment in pandas dataframe. By Pranit Sharma Last updated : October 03, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Problem statement

We are given a DataFrame df with some columns and we want to create a new column based on some previous columns.

We want to apply some conditions like if the value of a column is less then some specific value then the value of a new column is some new specific value. If the value of that column is some other specific value then the value of the new column would be some new specific value and so on.

Vectorize conditional assignment

We will use pandas.DataFrame.loc property of pandas so that we can access the exact element that fits the condition and we can set the value of a new column for each value of the old column.

The pandas.DataFrame.loc property is a type of data selection method which takes the name of a row or column as a parameter. To perform various operations using the pandas.DataFrame.loc property, we need to pass the required condition of rows and columns in order to get the filtered data.

Let us understand with the help of an example,

Python program to vectorize conditional assignment in pandas dataframe

The output of the above program is:

Example: Vectorize conditional assignment in pandas dataframe

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  • Pandas: Convert from datetime to integer timestamp
  • Add multiple columns to pandas dataframe from function
  • Adding a column in pandas dataframe using a function
  • Adding calculated column in Pandas
  • How to get first and last values in a groupby?
  • How to combine multiple rows of strings into one using pandas?
  • How can I extract the nth row of a pandas dataframe as a pandas dataframe?
  • Pandas Dataframe Find Rows Where all Columns Equal
  • Return max of zero or value for a pandas DataFrame column
  • Find first non-null value in column
  • Pandas add column to groupby dataframe
  • Remove rows in less than a certain value
  • Pandas DataFrame Diagonal
  • How to set/get pandas.DataFrame to/from Redis?
  • Make pandas DataFrame to a dict and dropna
  • Pandas Correlation Groupby
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  • How to divide two columns element-wise in a pandas dataframe?
  • How do I find the iloc of a row in pandas dataframe?
  • Pandas: Calculate moving average within group
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  • Reverse a get dummies encoding in pandas
  • Setting values on a copy of a slice from a dataframe
  • Removing newlines from messy strings in pandas dataframe cells
  • pd.NA vs np.nan for pandas
  • Pandas rank by column value
  • Pandas: selecting rows whose column value is null / None / nan
  • Best way to count the number of rows with missing values in a pandas DataFrame
  • Splitting dataframe into multiple dataframes based on column values and naming them with those values
  • Pandas: Extend Index of a DataFrame setting all columns for new rows to NaN?
  • Quickest way to swap index with values
  • How do pandas Rolling objects work?
  • Reversal of string.contains in pandas
  • Writing pandas DataFrame to JSON in unicode
  • Pandas: Conditional Sum with Groupby
  • Removing Rows on Count condition
  • Pandas combine two strings ignore nan values
  • Changing row index of pandas dataframe
  • Pandas fill missing values in dataframe from another dataframe
  • Replace part of the string in pandas dataframe
  • Pandas groupby and qcut
  • Pandas count null values in a groupby method
  • Pandas DataFrame save as HTML page
  • Transform vs. aggregate in Pandas
  • How can I iterate through two Pandas columns?
  • How to remove illegal characters so a dataframe can write to Excel?
  • Where is pandas.tools?
  • 'DataFrame' object has no attribute 'as_matrix
  • Stack two pandas dataframes
  • Groupby with User Defined Functions in Pandas
  • Merge multi-indexed with single-indexed dataframes in pandas
  • Sum across all NaNs in pandas returns zero
  • Difference between dtype and converters in pandas.read_csv()
  • Normalize dataframe by group
  • Pandas dataframe select row by max value in group
  • How to select rows that do not start with some str in pandas?
  • How to shift Pandas DataFrame with a multiindex?
  • What is correct syntax to swap column values for selected rows in a pandas data frame using just one line?
  • List with many dictionaries VS dictionary with few lists?
  • How to exclude a few columns from a DataFrame plot?
  • Groupby Pandas DataFrame and calculate mean and stdev of one column and add the std as a new column with reset_index
  • How can I reorder multi-indexed dataframe columns at a specific level?
  • Create bool mask from filter results in Pandas
  • How to turn a pandas dataframe row into a comma separated string?
  • How to concat two dataframes with different column names in pandas?
  • pandas.DataFrame.hist() Method
  • Reading two csv files and appending them into a new csv file
  • What is the difference between save a pandas dataframe to pickle and to csv?
  • Dropping time from datetime in Pandas
  • Map dataframe index using dictionary
  • Pandas: Get values from column that appear more than X times
  • Quickly drop dataframe columns with only one distinct value
  • How to flatten multilevel/nested JSON?
  • What does the group_keys argument to pandas.groupby actually do?
  • Extract int from string in Pandas
  • Get week start date (Monday) from a date column in Pandas?
  • Creating a new column in Pandas by using lambda function on two existing columns
  • When to use Category rather than Object?
  • How do I subtract the previous row from the current row in a pandas dataframe and apply it to every row; without using a loop?
  • Pandas: Replace zeros with previous non zero value
  • Pandas: Rounding when converting float to integer
  • How to get the index of ith item in pandas.Series or pandas.DataFrame?
  • Select non-null rows from a specific column in a DataFrame and take a sub-selection of other columns
  • How to map a function using multiple columns in pandas?
  • Count by unique pair of columns in pandas
  • Pandas: DataFrame stack multiple column values into single column
  • How to get a single value as a string from pandas dataframe?
  • Pandas: pd.Series.isin() performance with set versus array
  • Pandas text matching like SQL's LIKE?
  • Exception Handling in Pandas .apply() Function
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  • How to save image created with 'pandas.DataFrame.plot'?
  • Pandas: Assign an index to each group identified by groupby
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  • How can I group by month from a date field using Python and Pandas?
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  • Pandas DataFrame concat / update ('upsert')?
  • How to Pandas fillna() with mode of column?
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  • Pandas data frame transform INT64 columns to boolean
  • How to save in *.xlsx long URL in cell using Pandas?
  • How to map numeric data into categories / bins in Pandas dataframe?
  • Cumsum as a new column in an existing Pandas dataframe
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  • map() function inserting NaN, possible to return original values instead?
  • Pandas: reset_index() after groupby.value_counts()
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  • How to calculate 1st and 3rd quartiles in pandas dataframe?
  • How to check if a value is in the list in selection from pandas dataframe?
  • How to convert list of model objects to pandas dataframe?
  • How to get value counts for multiple columns at once in Pandas DataFrame?
  • How to one-hot-encode from a pandas column containing a list?
  • How to check if a column in a pandas dataframe is of type datetime or a numerical?
  • Pandas: Split dataframe into two dataframes at a specific row
  • Pandas: Subtracting two date columns and the result being an integer
  • Pass percentiles to pandas agg() method
  • Performant cartesian product (CROSS JOIN) with pandas
  • Pandas: Changing some column types to categories
  • Pandas: Flatten a dataframe to a list
  • Shuffling/Permutating a DataFrame in pandas
  • Stratified Sampling in Pandas
  • Getting the integer index of a pandas dataframe row fulfilling a condition
  • How to Read Specific Columns from Excel File?
  • Add value at specific iloc into new dataframe column in pandas
  • Pandas: Missing required dependencies
  • Store numpy.array() in cells of a Pandas.DataFrame()
  • How to find count of distinct elements in dataframe in each column?
  • Pandas: How to remove nan and -inf values?
  • Convert Pandas dataframe to Sparse Numpy Matrix Directly
  • Comparing previous row values in Pandas DataFrame
  • Melt the Upper Triangular Matrix of a Pandas DataFrame
  • Output different precision by column with pandas.DataFrame.to_csv()?
  • Pandas: Distinction between str and object types
  • How to find local max and min in pandas?
  • How to fix 'Passing list-likes to .loc or [] with any missing labels is no longer supported'?
  • How to retrieve name of column from its index in Pandas?
  • How to calculate intraclass correlation coefficient in Python?
  • How to remove outliers in Python?
  • How to perform equal frequency binning in Python?
  • How to perform multidimensional scaling in Python?
  • How to perform data binning in Python?
  • How to create frequency tables in Python?
  • How to create a contingency table in Python?
  • How to calculate relative frequency in Python?
  • How to perform bivariate analysis in Python?
  • Python - Create a pandas series from an array
  • Python - Create a pandas series from a scalar value
  • Python - Create a pandas series from a dictionary

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COMMENTS

  1. 5 ways to apply an IF condition in Pandas DataFrame

    Applying an IF condition in Pandas DataFrame Let's now review the following 5 cases: (1) IF condition - Set of numbers Suppose that you created a DataFrame in Python that has 10 numbers (from 1 to 10). You then want to apply the following IF conditions: If the number is equal or lower than 4, then assign the value of 'True'

  2. Set Pandas Conditional Column Based on Values of Another Column

    Using Pandas loc to Set Pandas Conditional Column Pandas loc is incredibly powerful! If you need a refresher on loc (or iloc), check out my tutorial here. Pandas' loc creates a boolean mask, based on a condition. Sometimes, that condition can just be selecting rows and columns, but it can also be used to filter dataframes.

  3. python

    python - If-else conditional assignment in pandas - Stack Overflow If-else conditional assignment in pandas Ask Question Asked 5 years, 10 months ago Modified 5 years, 10 months ago Viewed 8k times 8 I want to assign values to a column depending on the values of an already-existing column.

  4. Conditional Selection and Assignment With .loc in Pandas

    Dec 9, 2020 Photo by Qinghong Shen on Unsplash There are many different ways to select data in Pandas, but some methods work better than others. In this piece, we'll go over how to edit your DataFrames based on conditional statements using the .loc method.

  5. Ways to apply an if condition in Pandas DataFrame

    Pandas provides several methods to apply the if condition to a DataFrame, and the choice of method depends on the complexity of the condition and the desired outcome. In this article, we will explore various ways of applying the if condition to a DataFrame in Pandas. Apply an If Condition in Pandas DataFrame

  6. 5 Ways to Apply If-Else Conditional Statements in Pandas

    Method 1: Use the numpy.where () function The numpy.where () function is an elegant and efficient python function that you can use to add a new column based on 'true' or 'false' binary conditions. The syntax looks like this: np.where (condition, value if condition is true, value if condition is false)

  7. How to Apply the If-Else Condition in a Pandas DataFrame

    Use DataFrame.apply () to Apply the if-else Condition in a Pandas DataFrame in Python. The apply () method uses the data frame's axis (row or column) to apply a function. We can make our defined function that consists of if-else conditions and apply it to the Pandas dataframe. Here, we have defined a function assign_Result () and applied it ...

  8. Efficient Conditional Logic on Pandas DataFrames

    Conditionally Create or Assign Columns on Pandas DataFrames | by Louis Chan | Towards Data Science Member-only story Data Science, Programming, Pandas, Efficiency Efficient Implementation of Conditional Logic on Pandas DataFrames Time to stop being too dependent on .iterrows () and .apply () Louis Chan · Follow Published in Towards Data Science ·

  9. Using If-Else Statements in Pandas: A Practical Guide [+ Examples]

    The .apply method works well for multi-conditional scenarios like assigning multiple letter grades. If the evaluation is binary, however, you can simplify the workflow with .loc. 2. The .loc Method. ... Python, combined with its pandas and NumPy libraries, offers several strategies to incorporate if-else statements and their underlying logic ...

  10. Add a Column in a Pandas DataFrame Based on an If-Else ...

    This function takes three arguments in sequence: the condition we're testing for, the value to assign to our new column if that condition is true, and the value to assign if it is false. It looks like this: np.where (condition, value if condition is true, value if condition is false)

  11. Conditional operation on Pandas DataFrame columns

    Solution #1: We can use conditional expression to check if the column is present or not. If it is not present then we calculate the price using the alternative column. Python3 import pandas as pd df = pd.DataFrame ( {'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product': ['Umbrella', 'Mattress', 'Badminton', 'Shuttle'],

  12. pandas.DataFrame.assign

    pandas.DataFrame.assign #. pandas.DataFrame.assign. #. Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns.

  13. Pandas Conditional Selection and Modifying DataFrames

    Data Analysis with Python; Data Analysis with Python; Pandas Conditional Selection and Modifying DataFrames. Instead of using notebooks.ai like it shows in the video, you can use Google Colab instead. ... What will the following code print out? import pandas as pd certificates_earned = pd. DataFrame ({'Certificates': [8, 2, 5, 6] ...

  14. Conditional Statements in Python

    It allows for conditional execution of a statement or group of statements based on the value of an expression. The outline of this tutorial is as follows: First, you'll get a quick overview of the if statement in its simplest form.

  15. Python's Assignment Operator: Write Robust Assignments

    Table of Contents Assignment Statements and the Assignment Operator The Assignment Statement Syntax The Assignment Operator Assignments and Variables Other Assignment Syntax Assignment Statements in Action Initializing and Updating Variables Making Multiple Variables Refer to the Same Object Updating Lists Through Indices and Slices

  16. A Quick and Easy Guide to Conditional Formatting in Pandas

    One way to conditionally format your Pandas DataFrame is to highlight cells which meet certain conditions. To do so, we can write a simple function and pass that function into the Styler object using .apply () or .applymap (): .applymap (): applies a function to the DataFrame element-wise;

  17. python

    One line if-condition-assignment Ask Question Asked 12 years, 4 months ago Modified 11 months ago Viewed 484k times 209 I have the following code num1 = 10 someBoolValue = True I need to set the value of num1 to 20 if someBoolValue is True; and do nothing otherwise. So, here is my code for that num1 = 20 if someBoolValue else num1

  18. Python

    Vectorize conditional assignment. We will use pandas.DataFrame.loc property of pandas so that we can access the exact element that fits the condition and we can set the value of a new column for each value of the old column. The pandas.DataFrame.loc property is a type of data selection method which takes the name of a row or column as a parameter.

  19. python

    1 Answer Sorted by: 1 Use boolean indexing on both sides, and remove index alignment by converting to_numpy array: m = df ['C'] df.loc [m, ['A', 'B']] = df.loc [m, ['D', 'E']].to_numpy () Or change the column names with set_axis: df.loc [df ['C'], ['A', 'B']] = df [ ['D', 'E']].set_axis ( ['A', 'B'], axis=1) Output: