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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

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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.

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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. 

conditional assignment python dataframe

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DataFrames with Conditionals

The use of conditionals allows us to select a subset of rows based on the value in each row. Writing a conditional to select rows based on the data in a single column is straightforward and was used when we selected all of the courses taught by the Statistics department with the following code:

The subset of rows where the Subject is exactly equal to STAT (57 rows).

Complex Conditionals with Multiple Parts

As we want to answer more complex questions, we need increasingly complex conditionals. To help understand how a computer works, you may be familiar with the idea that computers ultimately only think in zeros and ones:

  • When a computer stores a zero, we consider that to be False .
  • When a computer stores a one, we consider that to be True .

When we use conditionals, we are assigning a truth value to every single row in the DataFrame.

  • With our conditional df[df.Subject == "STAT"] , all rows where the Subject data was "STAT" was assigned a truth value of True and kept in the final result; all other rows were labeled False and discarded.

All programming languages allows us to combine conditionals together in two key ways: with an AND ( & ) or with an OR ( | ).

Multiple Conditionals Joined with AND ( & )

When we combine two conditionals, we can ask Python to keep only the result where the first conditional AND the second conditional are both True .

Writing a conditional with multiple parts requires the use of parenthesis around each individual conditional and an operation joining the two conditionals together. For example, using the Course Catalog dataset , we want all of the courses that are taught by Computer Science ( CS ) with a course number less than 300:

Both the first ( Subject is exactly equal to "CS" ) and second ( Number is less than 300 ) conditionals are checked independently. Since an AND ( & ) is used to join these two conditionals, the final truth value is True only when both conditionals are True :

All CS courses with course numbers less than 300 (17 rows).

Python allows us to continue to apply conditionals together infinitely long -- so it's no problem to have three conditionals:

All CS courses with course numbers less than 300 and exactly 3 credit hours (6 rows).

Multiple Conditionals Joined with OR ( | )

Alternatively, Python can combine two conditionals together and keep the result when either the first conditional OR the second conditional is True (this includes when they're both True as well!). There are two major applications when this is useful:

  • Selecting multiple values of data from the same column (ex: all courses in "ARTD" OR "ARTE" OR "ARTF" ).
  • Selecting multiple values from different columns and keeping all matches (ex: all courses in "PSYC" OR courses that are only 1 credit hour).

Selecting Multiple Values of Data from the Same Column

Looking at the first example above, the University of Illinois has a lot of courses in art across many different sub-areas of art including: Art Design ( "ARTD" ), Art Education ( "ARTE" ), Art Foundation ( "ARTF" ), Art History ( "ARTH" ), and Art Studio ( "ARTS" ).

To include ALL courses from all five sub-areas of art listed above, we must join them together with an OR ( | ). Notice that it is necessary to specify each conditional completely each time even though we are always comparing the subject since Python has to evaluate each conditional independently and then combine the results together:

All courses in any subjects ARTD, ARTE, ARTF, ARTH, OR ARTS (221 rows).

Selecting Multiple Values from Different Columns and Keeping All Matches

To be considered a "full-time student" at most universities, you must be enrolled in at least 12 credit hours . If you are only enrolled in 11 credit hours, you may be interested in any course that will bump you up to exactly 12 credit hours (ex: a course worth exactly one credit hour) or a course you may be interested in (ex: something from the psychology ( "PSYC" ) department).

To include ALL of the results of all courses that are either one credit hour OR in the psychology department, we need an OR :

All courses that are exactly one credit hour OR in the psychology department (490 rows).

Combining ANDs and ORs

The most complex conditionals will require a combination of both AND and OR statements. These can get incredibly tricky, but we can remember that Python will always process conditionals by only combining two conditionals together at a time.

Since Python combines only two conditionals together at any given time, it is critical we use parenthesis to ensure we specify the order that we want these conditionals combined. For example, let's explore only junior level (300-399) courses in Chemistry or Physics . To do so:

  • The subject of the course must be CHEM or PHYS .
  • The course number must be greater than or equal to 300 .
  • The course number must also be less than 400 .

Naively writing this conditional results in the following code:

Default Order of Evaluation: AND before OR

If we do not use additional parenthesis, Python will always combine the ANDs first and then the ORs and will do so in left-to-right order. This means that:

The first set of two conditionals combined will be the first AND conditional: (df.Subject == "PHYS") & (df.Number >= 300) . The result contains all courses in PHYS with a number larger than 300.

The second set of two conditionals will be the result from #1 with the second AND : (Result of Step #1) & (df.Number < 400) . The result contains all courses in PHYS with a number from 300-399.

The final set of conditionals will be combined using OR : (df.Subject == "CHEM") | (Result of Step #2) . Since this is an OR , the result is ALL CHEM courses and then only the PHYS courses in the number 300-399.

We can verify our result by running the code:

The output of incorrect logic that does use parenthesis, which includes 500-level PHYS courses (92 rows).

Notice that the code appears correct until we scroll down ! The courses in Chemistry start at 300, but the last five rows show us that the courses in Physics include 500-level courses -- yikes!

Order of Evaluation: Using Parenthesis to Specify Order

Python uses parenthesis in a similar way to basic mathematics where the inner-most operations are done first. In our example, we want to make sure that all Chemistry and Physics courses are combined first, and only then can we limit the range of course numbers to the junior level.

By grouping both of these logical operations together, our new conditional can be thought of as a combination of two complex conditionals:

(df.Subject == "CHEM") | (df.Subject == "PHYS") , selecting only that are Chemistry OR Physics

(df.Number >= 300) & (df.Number < 400) , selecting only courses between 300 AND 399.

Joining these two conditionals together with an AND results in the exact output we expect:

All 300-level courses in chemistry or physics (11 rows).

Example Walk-Throughs with Worksheets

Video 1: dataframe conditionals using the party dataset.

  • Download Blank Worksheet (PDF)

Video 2: DataFrame Conditionals using The Berkeley Dataset

Video 3: DataFrame Conditionals using The Course Catalog Dataset

Practice Questions

conditional assignment python dataframe

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Home » Python

Conditional selection in the DataFrame | Pandas DataFrame

Here, we are going to learn about the conditional selection in the Pandas DataFrame in Python , Selection Using multiple conditions , etc. Submitted by Sapna Deraje Radhakrishna , on January 06, 2020

Conditional selection in the DataFrame

Consider the following example,

If we use < symbol on a DataFrame, like >0 , the values in the dataFrame is compared against 0 and returned with True/False.

Now, assign the df>0 to a Boolean value called bool_df

Pass bool_df to df , in the below we can see that the values which were True have their original value and where it is False, we have a NAN. Using this approach, we can use the conditional selection in dataFrame.

The above can be achieved in single line,

Instead of passing an entire dataFrame, pass only the row/column and instead of returning nulls what that's going to do is return only the rows/columns of a subset of the data frame where the conditions are True.

Take a look at the 'A' column, here the value against 'R', 'S', 'T' are less than 0 hence you get False for those rows,

Use this series of Boolean values corresponding to rows to filter out rows based off of a column values and that means if the series is passed into a dataFrame using bracket notation, the rows of A which have True value will only be returned (no null values returned).

Consider some more examples of conditional selection, grab all rows from df where D<0

Retrieving the subset dataFrame, step wise

Retrieving the subset dataFrame, in single step

Retrieving multiple columns from dataFrame

Though it seems to be a little confusing to use one-liners, it is a preferred way, since using multiple steps the code takes more memory with each variable defined. However, until one is comfortable it is good to break it down to multiple steps.

Selection Using multiple conditions

The normal approach in python to implement multiple conditions is by using 'and' operator . However, if we use the 'and' operator in the pandas function we get an 'ValueError: The truth value of a Series is ambiguous.' Consider the below example

The reason for the above error is, in python the 'and' operator can deal with the single instance of Boolean values and not multiple instances. The df['D'] results in multiple instances of Boolean value, as shown below,

In Pandas, in order to use and logical operation we have to use &

'or' operation, use '|'

Resetting the index

In order to reset the index, use the method reset_index() as explained in below example,

In the above example, the index is reset to numerical values and the existing indexes are reset to a column 'index'.

The method reset_index() doesn't occur in place, unless we pass an argument (inplace=True), as explained in below example,

Setting the index

Create a new column as mentioned below,

Set the index, sets in-place (cannot be reverted)

All Python Pandas Programs

  • Create a MultiIndex with names of each of the index levels in Python Pandas
  • How to get the levels in MultiIndex in Python Pandas?
  • How to get the name of levels in MultiIndex in Python Pandas?
  • Python Pandas | Set levels on a MultiIndex
  • Python Pandas | Swap levels of a MultiIndex
  • Rearrange levels using level name in MultiIndex in Python Pandas
  • Return MultiIndex with multiple levels removed using the level names in Python Pandas
  • Create a DataFrame with levels of MultiIndex as columns and substitute index level names in Python Pandas
  • Add a new column to existing DataFrame by declaring a new list as a column in Python Pandas
  • Add a new column to existing DataFrame using DataFrame.insert()
  • Add a new column to existing DataFrame using Dataframe.assign()
  • Add a new column to existing DataFrame using a dictionary
  • How to delete a column from a Pandas DataFrame?
  • How to rename columns in Pandas DataFrame?
  • How to select rows from a DataFrame based on column values?
  • How to change the order of DataFrame columns?
  • How to Get the List of Pandas DataFrame Column Headers?
  • How to get the number of rows in DataFrame?
  • How to select multiple rows from a Pandas DataFrame?
  • How to count the NaN values in a column in Pandas DataFrame?
  • Set value for particular cell in Pandas DataFrame using index
  • Python | Shuffle Pandas DataFrame Rows
  • How to Convert Index to Column in Pandas DataFrame?
  • Create an Empty Pandas DataFrame and Fill It
  • Combine two columns of text in Pandas DataFrame
  • Drop Rows from Pandas DataFrame Based on Column Value
  • Convert List of Dictionaries to a Pandas DataFrame
  • How to pretty-print an entire Pandas DataFrame?
  • Write a Pandas DataFrame to a CSV File
  • Difference Between loc and iloc Properties in Pandas DataFrame
  • Expand Output Display to See More Columns in Pandas DataFrame
  • How to Use 'NOT IN' Filter in Pandas?
  • Import Multiple CSV Files into Pandas DataFrame
  • Export Pandas DataFrame to CSV without Index and Header
  • How to convert pandas DataFrame to NumPy array?
  • Check for NaN Values in Pandas DataFrame
  • Count Column-wise NaN Values in Pandas DataFrame
  • How to fix UnicodeDecodeError when reading CSV file in Pandas with Python?
  • How to Replace NaN Values with Zeros in Pandas DataFrame?
  • ValueError: If using all scalar values, you must pass an index, How to Fix it?
  • Pandas | Apply a Function to Multiple Columns of DataFrame
  • Convert DataFrame Column Type from String to Datetime
  • Create Pandas DataFrame from a String
  • How to Add an Empty Column to a DataFrame?
  • Get First Row of a Pandas DataFrame
  • Sorting columns in pandas DataFrame based on column name
  • Count the frequency that a value occurs in a DataFrame column
  • Python Pandas: Get index of rows which column matches certain value
  • How to check whether a Pandas DataFrame is empty?
  • How to group DataFrame rows into list in pandas groupby?
  • How to filter pandas DataFrame by operator chaining?
  • Python Pandas: Conditional creation of a series/DataFrame column
  • Selecting/excluding sets of columns in pandas
  • How to use pivot function in a pandas DataFrame?
  • How to apply a function to a single column in pandas DataFrame?
  • How to flatten a hierarchical index in columns?
  • How to remap values in pandas using dictionaries?
  • How to perform pandas groupby() and sum()?
  • Pandas get rows which are NOT in other DataFrame
  • Pandas read in table without headers
  • Pandas: Drop a level from a multi-level column index
  • Get column index from column name in Python pandas
  • How to keep only date part when using pandas.to_datetime?
  • How to extract month and year separately from datetime in pandas?
  • How to replace NaN with blank/empty string?
  • How to drop a list of rows from Pandas DataFrame?
  • How to select DataFrame rows between two dates?
  • How to drop infinite values from DataFrames in Pandas?
  • How to add a column to DataFrame with constant value?
  • Split (explode) pandas DataFrame string entry to separate rows
  • How to select with complex criteria from pandas DataFrame?
  • How to count unique values per groups with Pandas?
  • How to convert floats to ints in Pandas?
  • How to insert a given column at a specific position in a Pandas DataFrame?
  • How to update a DataFrame in pandas while iterating row by row?
  • How to take column slices of DataFrame in pandas?
  • How to select rows with one or more nulls from a Pandas DataFrame without listing columns explicitly?
  • How to convert column value to string in pandas DataFrame?
  • How to find the installed pandas version?
  • How to merge two DataFrames by index?
  • How to obtain the element-wise logical NOT of a Pandas Series?
  • How to split a DataFrame string column into two columns?
  • How to add x and y labels to a pandas plot?
  • How to find row where values for column is maximal in a Pandas DataFrame?
  • How to apply Pandas function to column to create multiple new columns?
  • How to convert Pandas DataFrame to list of Dictionaries?
  • How to extract specific columns to new DataFrame?
  • Why should we make a copy of a DataFrame in Pandas?
  • How to get plot correlation matrix using Pandas?
  • How to merge multiple DataFrames on columns?
  • Python Pandas groupby sort within groups
  • How to create an empty DataFrame with only column names?
  • How to filter Pandas DataFrames on dates?
  • What is the difference between join and merge in Pandas?
  • How to determine whether a Pandas Column contains a particular value?
  • How to get rid of 'Unnamed: 0' column in a pandas DataFrame read in from CSV file?
  • How to read a large CSV file with pandas?
  • Label encoding across multiple columns in scikit-learn
  • How to read text files with Python Pandas?
  • How to select rows in pandas MultiIndex DataFrame?
  • How to delete the first three rows of a DataFrame in Pandas?
  • Boolean Indexing in Pandas
  • How to apply logical operators for Boolean indexing in Pandas?
  • How to set number of maximum rows in Pandas DataFrame?
  • How to calculate average/mean of Pandas column?
  • How to add header row to a Pandas DataFrame?
  • How to convert multiple lists into DataFrame?
  • How to remove duplicate columns in Pandas DataFrame?
  • How to save a Seaborn plot into a file?
  • How to show all columns' names on a large Pandas DataFrame?
  • Pandas: How to replace all values in a column, based on condition?
  • How to Map True/False to 1/0 in a Pandas DataFrame?
  • How to perform random row selection in Pandas DataFrame?
  • How to display Pandas DataFrame of floats using a format string for columns?
  • How to read specific sheet content when there are multiple sheets in an excel file?
  • How to search for 'does-not-contain' on a DataFrame in pandas?
  • How to create separate rows for each list item where the list is itself an item of a pandas DataFrame column?
  • How to Format or Suppress Scientific Notation in NumPy?
  • How to groupby elements of columns with NaN values?
  • How to find which columns contain any NaN value in Pandas DataFrame?
  • How to filter rows in pandas by regex?
  • How to apply a function with multiple arguments to create a new Pandas column?
  • How to retrieve the number of columns in a Pandas DataFrame?
  • How to replace blank values (white space) with NaN in Pandas?
  • How to concatenate a list of pandas DataFrames together?
  • How to get a list of all the duplicate items using Pandas in Python?
  • What is the difference between a Pandas Series and a DataFrame?
  • How to get first row of each group in Pandas DataFrame?
  • How to get topmost N records within each group of a Pandas DataFrame?
  • Pandas dataframe fillna() only some columns in place
  • How to create a dictionary of two Pandas DataFrames columns?
  • How to append only last row of a DataFrame to a new DataFrame?
  • How to sort rows in pandas DataFrame?
  • How to add pandas DataFrame to an existing CSV file?
  • How to compare two DataFrames and output their differences side-by-side?
  • How to insert rows in pandas DataFrame?
  • How to read a .xlsx file using the pandas Library?
  • How to keep index when using pandas merge?
  • Drop columns whose name contains a specific string from pandas DataFrame
  • How to select every nth row in pandas?
  • Python Pandas: Merge only certain columns
  • How to delete the last row of data of a pandas DataFrame?
  • Find the column name which has the maximum value for each row
  • How to find unique values from multiple columns in pandas?
  • How to modify a subset of rows in a pandas DataFrame?
  • How to replace text in a string column of a Pandas DataFrame?
  • How to get total of Pandas column?
  • When should/shouldn't we use pandas apply() in our code?
  • How to convert epoch time to datetime in pandas?
  • How to get the first column of a pandas DataFrame as a Series?
  • Concatenate strings from several rows using pandas groupby
  • How to estimate how much memory a Pandas' DataFrame will need?
  • How to print very long string completely in pandas DataFrame?
  • How to select distinct across multiple DataFrame columns in pandas?
  • How to fill a DataFrame row by row?
  • How to create a DataFrame of random integers with Pandas?
  • How to use corr() to get the correlation between two columns?
  • Make Pandas DataFrame apply() use all cores
  • What is dtype('O') in Pandas?
  • Select Pandas rows based on list index
  • NumPy Array Copy vs View
  • Unique combinations of values in selected columns in Pandas DataFrame and count
  • How to prepend a level to a pandas MultiIndex?
  • How to check the dtype of a column in Python Pandas?
  • How to select all columns whose name start with a particular string in pandas DataFrame?
  • How to Convert a DataFrame to a Dictionary?
  • How to Read First N Rows from DataFrame in Pandas?
  • Appending a list or series to a pandas DataFrame as a row?
  • Making Heatmap from Pandas Dataframe
  • How to Compare Two Columns of Pandas DataFrame?
  • Python Pandas: Replace NaN in one column with value from corresponding row of second column
  • How to make pandas DataFrame column headers all lowercase?
  • GroupBy pandas DataFrame and select most common value
  • Split a large pandas DataFrame
  • How do you filter pandas DataFrames by multiple columns?
  • Understanding inplace=True in Pandas
  • How to return the index of filtered values in pandas DataFrame?
  • What is the most efficient way to check if a value exists in a NumPy array?
  • Add column in DataFrame from list
  • What is the fast way to drop columns in pandas DataFrame?
  • How to extract NumPy arrays from specific column in pandas frame and stack them as a single NumPy array?
  • Dropping a row in pandas DataFrame if any value in row becomes 0
  • Selecting pandas column by location
  • Data Normalization in Pandas
  • Set Order of Columns in Pandas DataFrame
  • Creating a new column based on if-elif-else condition
  • How to perform cartesian product in pandas?
  • How to find common element or elements in multiple DataFrames?
  • Find the max of two or more columns with pandas?
  • How to select rows in a DataFrame between two values in Python Pandas?
  • Pandas DataFrame groupby datetime month
  • Convert categorical data in pandas dataframe
  • Add column with number of days between dates in DataFrame pandas
  • Difference between merge() and concat() in pandas
  • Update Index After Sorting Pandas DataFrame
  • Strings in a DataFrame, but dtype is object
  • Move column by name to front of table in pandas
  • How to plot multiple horizontal bars in one chart with matplotlib?
  • Pandas: Change data type from series to string
  • Drop rows containing empty cells from a pandas DataFrame
  • Apply function to each cell in DataFrame
  • Appending pandas DataFrames generated in a for loop
  • How to pass another entire column as argument to pandas fillna()?
  • Python pandas DataFrame, is it pass-by-value or pass-by-reference?
  • How to create a new column from the output of pandas groupby().sum()?
  • Pandas aggregate count distinct
  • Does pandas iterrows have performance issues?
  • Import pandas DataFrame column as string not int
  • Construct pandas DataFrame from items in nested dictionary
  • Plotting categorical data with pandas and matplotlib
  • NumPy isnan() fails on an array of floats
  • Can Pandas plot a histogram of dates?
  • How to Shift a Column in Pandas Dataframe?
  • Extract first and last row of a DataFrame in Pandas
  • Pandas: Filling missing values by mean in each group
  • How to delete all columns in DataFrame except certain ones?
  • How to Merge a Series and DataFrame?
  • Pandas: Convert index to datetime
  • Apply Function on DataFrame Index
  • How to strip the whitespace from Pandas DataFrame headers?
  • DataFrame object has no attribute sort
  • How to replace negative numbers in Pandas Data Frame by zero?
  • Lambda including if, elif and else
  • Pandas: Find percentile stats of a given column
  • Count number of non-NaN entries in every column of Dataframe
  • Access Index of Last Element in pandas DataFrame in Python
  • Pandas: Create two new columns in a DataFrame with values calculated from a pre-existing column
  • Pandas crosstab() function with example
  • How to sum values in a column that matches a given condition using Pandas?
  • How to use melt function in pandas?
  • How to add main column header for multiple column headings?
  • Convert Dataframe column of list with dictionaries into separate columns and expand Dataframe
  • Adding a column that result of difference in consecutive rows in Pandas
  • How to Add Incremental Numbers to a New Column Using Pandas?
  • Convert Select Columns in Pandas Dataframe to NumPy Array
  • How to convert rows in DataFrame in Python to dictionaries?
  • Pandas: Apply function that returns multiple values to rows in pandas DataFrame
  • Pandas: Sum up multiple columns into one column without last column
  • Transforming a DataFrame
  • Pandas column values to columns
  • How to group a series by values in pandas?
  • Appending Column Totals to a Pandas DataFrame
  • Converting a pandas date to week number
  • Make new column in Pandas DataFrame by adding values from other columns
  • Find length of longest string in Pandas DataFrame column
  • Finding non-numeric rows in dataframe in pandas
  • Multiply two columns in a pandas dataframe and add the result into a new column
  • Python Pandas: Pivot table with aggfunc = count unique distinct
  • How to simply add a column level to a pandas dataframe?
  • Python Pandas: Rolling functions for GroupBy object
  • Merge multiple column values into one column in Python pandas
  • Create column of value_counts in Pandas dataframe
  • Pandas get frequency of item occurrences in a column as percentage
  • Pandas: 'DatetimeProperties' object has no attribute 'isocalendar'
  • Python Pandas: How to calculate 1st and 3rd quartiles?
  • Python Pandas: Convert commas decimal separators to dots within a Dataframe
  • Compute row average in pandas
  • Python Pandas: Cumulative sum and percentage on column
  • Python - Split pandas dataframe based on groupby
  • Python - Drop all data in a pandas dataframe
  • How to sort a dataFrame in python pandas by two or more columns?
  • Python - How to calculate mean values grouped on another column in Pandas?
  • Python Pandas: Convert strings to time without date
  • Python - Create a categorical type of column in pandas dataframe
  • Python - Pandas 'describe' is not returning summary of all columns
  • Python - Pandas applying regex to replace values
  • Python - Pandas replace a character in all column names
  • Python - Dynamically evaluate an expression from a formula in Pandas
  • Python - Can pandas groupby aggregate into a list, rather than sum, mean, etc?
  • Python - Pandas sum across columns and divide each cell from that value
  • Python - Find all columns of dataframe in Pandas whose type is float, or a particular type
  • Python - Convert entire pandas dataframe to integers
  • Python Pandas - Get first letter of a string from column
  • Python - How to multiply columns by a column in Pandas?
  • Python - Set difference for pandas
  • Python Pandas: Flatten a list of dataframe
  • Python - Find out the percentage of missing values in each column in the given dataset
  • Python - Group by index and column in pandas
  • Python - How to update values in a specific row in a Pandas DataFrame?
  • Python - Create pandas dataframe from dictionary of dictionaries
  • How to perform CROSS JOIN with pandas dataframe?
  • Python Pandas - Find difference between two dataframes
  • How to replace an entire column on pandas dataframe?
  • Splitting at underscore in python and storing the first value
  • How to filter a pandas dataframe based on value counts?
  • Python - Get particular row as series from pandas dataframe
  • Python - List of Tuples to DataFrame Conversion
  • Python - How to convert pandas dataframe to a dictionary without index?
  • Python Pandas: Convert a column of list to dummies
  • Python - Count occurrences of False or True in a column in pandas
  • Python Pandas: Make a new column from string slice of another column
  • Python - Getting wider output in PyCharm's built-in console
  • Python - Change a column of yes or no to 1 or 0 in a pandas dataframe
  • Python - Replace all occurrences of a string in a pandas dataframe
  • Python - Rolling mean on pandas on a specific column
  • Python Pandas - Return only those rows which have missing values
  • Python - Get the mean across multiple pandas dataframes
  • Python - How to remove a pandas dataframe from another dataframe?
  • Python Pandas - Sort by group aggregate and column
  • Python Pandas - Update value if condition in 3 columns are met
  • Python Pandas - Start row index from 1 instead of zero without creating additional column
  • Python - Filter Pandas DataFrame by Time Index
  • Python - How do I round datetime column to nearest quarter hour?
  • How to copy or paste DataFrame from Stack Overflow into Python
  • Python - Add columns of different length in pandas
  • Python - Return max value from pandas dataframe, not based on column or rows but as a whole
  • Python - Get total number of hours from a Pandas Timedelta?
  • Python - Filter the columns in a pandas dataframe based on whether they are of type date or not
  • Python - Create a set from a series in pandas
  • Python - NumPy 'where' function multiple conditions
  • Python - How to insert pandas dataframe into database?
  • Python - Join or merge with overwrite in pandas
  • Python - USING LIKE inside pandas query
  • Python - How to add an extra row to a pandas dataframe?
  • Python - How to get the number of the most frequent values in a column?
  • Python - Pandas conditional rolling count
  • Python - Summing two columns in a pandas dataframe
  • Python - How to swap two dataframe columns?
  • Python - Pandas DataFrame Add Column to Index without Resetting
  • Python - Checking whether dataframe is copy or view in pandas
  • Python - Pandas Strip Whitespace
  • Python - Pandas apply function with two arguments to columns
  • Python - Using .loc with a MultiIndex in pandas
  • Python - Tilde Sign (~) in Pandas DataFrame
  • Python - Concat series onto dataframe with column name
  • Python - Splitting timestamp column into separate date and time columns
  • Python - Sorting by absolute value without changing the data
  • Python - Sort descending dataframe with pandas
  • Python - Extracting the first day of month of a datetime type column in pandas
  • Python - Accessing every 1st element of Pandas DataFrame column containing lists
  • Python - Appending two dataframes with same columns, different order
  • Python - Pandas dataframe.shift()
  • Python Pandas: Difference between pivot and pivot_table
  • Python - How to filter rows from a dataframe based on another dataframe?
  • Python - How to open a JSON file in pandas and convert it into DataFrame?
  • Python - Create hourly/minutely time range using pandas
  • Python - Set MultiIndex of an existing DataFrame in pandas
  • Python - How to transpose dataframe in pandas without index?
  • Python - Finding count of distinct elements in dataframe in each column
  • Python Pandas: Update a dataframe value from another dataframe
  • Python - Selecting Pandas Columns by dtype
  • Python - Logical operation on two columns of a dataframe
  • Python - Replace string/value in entire dataframe
  • Remove first x number of characters from each row in a column of a Python DataFrame
  • Python - Sorting columns and selecting top n rows in each group pandas dataframe
  • Python - How to do a left, right, and mid of a string in a pandas dataframe?
  • Python Pandas DataFrame: Apply function to all columns
  • Python - How to convert column with list of values into rows in pandas dataframe?
  • Python - How to query if a list-type column contains something?
  • Python - Calculate summary statistics of columns in dataframe
  • Python - Append an empty row in dataframe using pandas
  • Applying uppercase to a column in pandas dataframe
  • Drop non-numeric columns from a pandas dataframe
  • Fill nan in multiple columns in place in pandas
  • Filter dataframe based on index value
  • How to use pandas tabulate for dataframe?
  • Pandas converting row with UNIX timestamp (in milliseconds) to datetime
  • Pandas cut() Method with Example
  • Pandas DataFrame forward fill method (pandas.DataFrame.ffill())
  • pandas.DataFrame.set_flags() Method with Examples
  • Pandas factorize() Method with Example
  • Pandas qcut() Method with Example
  • Pandas series to dataframe using series indexes as columns
  • Pandas replacing strings in dataframe with numbers
  • Scaling numbers column by column with pandas
  • Python - How to get scalar value on a cell using conditional indexing?
  • Pandas compute mean or std over entire dataframe
  • Turn all items in a dataframe to strings
  • Repeat Rows in DataFrame N Times
  • Merge a list of dataframes to create one dataframe
  • Python - How to create a dataframe while preserving order of the columns?
  • Combine two pandas dataframes with the same index
  • Square of each element of a column in pandas
  • Convert whole dataframe from lowercase to uppercase with Pandas
  • How to set dtypes by column in pandas dataframe?
  • How to Calculate Cumulative Sum by Group (cumsum) in Pandas?
  • Programmatically convert pandas dataframe to markdown table
  • GroupBy results to dictionary of lists
  • Truncate timestamp column to hour precision in pandas dataframe
  • Pandas GroupBy get list of groups
  • Max and Min date in pandas groupby
  • Pandas filling NaNs in categorical data
  • Replace whole string if it contains substring in pandas
  • Pandas ValueError Arrays Must be All Same Length
  • Format a number with commas to separate thousands in pandas
  • Is there an ungroup by operation opposite to groupby in pandas?
  • How to insert a pandas dataframe to an already existing table in a database?
  • Ranking order per group in Pandas
  • Get all keys from GroupBy object in Pandas
  • Find unique values in a pandas dataframe, irrespective of row or column location
  • How to check if a variable is either a Python list, NumPy array, or pandas series?
  • Pandas, Future Warning: Indexing with multiple keys
  • Pandas DataFrame Resample
  • Pandas DataFrame asfreq() Method with Example
  • Check if all values in dataframe column are the same
  • How to remove numbers from string terms in a pandas dataframe?
  • Reset a column multiindex levels
  • Use pandas groupby() and apply() methods with arguments
  • How to get unique values from multiple columns in a pandas groupby?
  • Normalize rows of pandas dataframe by their sums
  • Subtract a year from a datetime column in pandas
  • What is the best way to sum all values in a pandas dataframe?
  • How to access the last element in a pandas series?
  • ImportError: No module named 'xlrd'
  • Adding dummy columns to the original dataframe
  • How to reset index pandas dataframe after dropna() pandas dataframe?
  • Mapping columns from one dataframe to another to create a new column
  • What does the term broadcasting mean in Pandas documentation?
  • Stop Pandas from converting int to float due to an insertion in another column
  • Split cell into multiple rows in pandas dataframe
  • Using pandas append() method within for loop
  • Selecting columns by list where columns are subset of list
  • Add a row at top in pandas dataframe
  • Counting the frequency of words in a pandas dataframe
  • Calculate new column as the mean of other columns in pandas
  • Pandas Assigning multiple new columns simultaneously
  • Slice Pandas DataFrame by Row
  • Convert DataFrame GroupBy object to DataFrame Pandas
  • Create multiple dataframes in loop
  • Pandas dataframe str.contains() AND operation
  • How to convert pandas series to tuple of index and value?
  • Pandas Groupby: Count and mean combined
  • Merge a list of pandas dataframes
  • Boolean indexing in pandas dataframes with multiple conditions
  • How to write specific columns of a DataFrame to a CSV?
  • Obtaining last value of dataframe column without index
  • Pandas, DF.groupby().agg(), column reference in agg()
  • Pandas Timedelta in Months
  • Iterate over pandas dataframe using itertuples
  • Pandas shift down values by one row within a group
  • Merge two dataframes based on multiple keys in pandas
  • Pandas dataframe remove constant column
  • Pandas combining two dataframes horizontally
  • Retrieve name of column from its index in pandas
  • Pandas pivot tables row subtotals
  • Pandas pivot table count frequency in one column
  • Pandas DataFrame merge summing column
  • Check if string in one column is contained in string of another column in the same row
  • Change multiple columns in pandas dataframe to datetime
  • Pandas replace multiple values one column
  • Pandas multilevel column names
  • How to use pandas cut() method?
  • How can I check if a Pandas dataframe's index is sorted?
  • Set values on the diagonal of pandas.DataFrame
  • Calculate average of every x rows in a table and create new table
  • How to convert a pandas DataFrame subset of columns AND rows into a numpy array?
  • Pandas split column into multiple columns by comma
  • Merge two python pandas dataframes of different length but keep all rows in output dataframe
  • When to apply(pd.to_numeric) and when to astype(np.float64)
  • Filter out groups with a length equal to one
  • Pandas compare next row
  • Index of non 'NaN' values in Pandas
  • Pandas combine two columns with null values
  • Pandas add column with value based on condition based on other columns
  • Drop row if two columns are NaN
  • Count and Sort with Pandas
  • How to delete all rows in a dataframe?
  • Create an empty MultiIndex
  • Pandas convert month int to month name
  • Unpivot Pandas Data
  • Absolute value for a column
  • Pandas dataframe create new columns and fill with calculated values from same dataframe
  • Keep other columns when using sum() with groupby
  • How to groupby consecutive values in pandas dataframe?
  • How to remove rows in a Pandas dataframe if the same row exists in another dataframe?
  • How to get tfidf with pandas dataframe?
  • Pandas count number of elements in each column less than x
  • Python - How to set column as date index?
  • Seaborn: countplot() with frequencies
  • SKLearn MinMaxScaler - scale specific columns only
  • Pandas integer YYMMDD to datetime
  • Select multiple ranges of columns in Pandas DataFrame
  • Random Sample of a subset of a dataframe in Pandas
  • Selecting last n columns and excluding last n columns in dataframe
  • Search for a value anywhere in a pandas dataframe
  • Pandas Number of Months Between Two Dates
  • Pandas remove everything after a delimiter in a string
  • Pandas difference between largest and smallest value within group
  • Add a new row to a pandas dataframe with specific index name
  • Sort dataframe by string length
  • Pandas groupby for zero values
  • Join two dataframes on common column
  • Vectorize conditional assignment in pandas dataframe
  • Pandas Group by day and count for each day
  • Pandas dataframe remove all rows where None is the value in any column
  • Missing data, insert rows in Pandas and fill with NAN
  • Pandas: Output dataframe to csv with integers
  • Pandas join dataframe with a force suffix
  • Pandas DataFrame: How to query the closest datetime index?
  • Sum of all the columns of a pandas dataframe with a wildcard name search
  • Pandas slice dataframe by multiple index ranges
  • Pandas Extract Number from String
  • Pandas groupby(), agg(): How to return results without the multi index?
  • Convert Series of lists to one Series in Pandas
  • Pandas groupby.apply() method duplicates first group
  • Pandas: Create dataframe from list of namedtuple
  • Reading excel to a pandas dataframe starting from row 5 and including headers
  • How do I remove rows with duplicate values of columns in pandas dataframe?
  • Pandas: Convert from datetime to integer timestamp
  • Add multiple columns to pandas dataframe from function
  • Adding a column in pandas dataframe using a function
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python – vectorize conditional assignment in pandas dataframe

Sure! I can help you with that. Vectorizing the conditional assignment in a pandas DataFrame means applying a condition to a column in the DataFrame and assigning a value based on that condition. This can be done efficiently by using NumPy’s vectorized operations. Here are 8 examples of how to vectorize conditional assignment in a pandas DataFrame, along with a step-by-step explanation: Example 1: Assign a fixed value to a column if a condition is met: python import pandas as pd import numpy as np df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) df['B'] = np.where(df['A'] > 3, 10, 0) Explanation: – We create a DataFrame with a column ‘A’. – Using the `np.where()` function, we apply the condition `df[‘A’] > 3` to the column ‘A’. – If the condition is True, the corresponding value in column ‘B’ is set to 10; otherwise, it is set to 0. Example 2: Assign values based on multiple conditions using logical operators: python df['C'] = np.where((df['A'] > 2) & (df['A'] < 5), 'yes', 'no') Explanation: – We apply multiple conditions to the column ‘A’ using logical operators `&` and `|`. – If the condition `(df[‘A’] > 2) & (df[‘A’] python df['D'] = np.where(df['A'] >= 3, 'high', np.where(df['A'] > 1, 'medium', 'low')) Explanation: – We use nested `np.where()` functions to assign values based on different conditions. – If the condition `df[‘A’] >= 3` is True, the corresponding value in column ‘D’ is set to ‘high’. – If the condition `df[‘A’] > 1` is True (since the previous condition is False), the corresponding value in column ‘D’ is set to ‘medium’. – Otherwise, the value is set to ‘low’. Example 4: Assign values based on a condition with multiple possible outcomes: python df['E'] = np.select([df['A'] > 3, df['A'] > 1], ['high', 'medium'], default='low') Explanation: – We use `np.select()` to assign values based on multiple conditions and corresponding outcomes. – If the condition `df[‘A’] > 3` is True, the corresponding value in column ‘E’ is set to ‘high’. – If the condition `df[‘A’] > 1` is True (since the previous condition is False), the corresponding value in column ‘E’ is set to ‘medium’. – Otherwise, the default value ‘low’ is assigned. Example 5: Assign values based on a condition by applying a function: python def assign_grade(score): if score >= 90: return 'A' elif score >= 80: return 'B' elif score >= 70: return 'C' else: return 'D' df['Grade'] = df['Score'].apply(assign_grade) Explanation: – We define a function `assign_grade()` that takes a score and returns a grade based on certain conditions. – We apply this function to the ‘Score’ column using the `apply()` method, and the result is assigned to a new column ‘Grade’. Example 6: Assign values based on conditions across multiple columns: python df['Pass'] = np.where((df['Score'] > 70) & (df['Attendance'] > 80), 'Yes', 'No') Explanation: – We apply conditions based on multiple columns (‘Score’ and ‘Attendance’) using logical operators. – If both the conditions `(df[‘Score’] > 70)` and `(df[‘Attendance’] > 80)` are True, the corresponding value in column ‘Pass’ is set to ‘Yes’; otherwise, it is set to ‘No’. Example 7: Assign values based on a condition using a lambda function: python df['Even/Odd'] = df['Number'].apply(lambda x: 'Even' if x % 2 == 0 else 'Odd') Explanation: – We use a lambda function to apply a condition directly to the ‘Number’ column. – If the condition `(x % 2 == 0)` is True, the corresponding value in column ‘Even/Odd’ is set to ‘Even’; otherwise, it is set to ‘Odd’. Example 8: Assign values based on a lookup from another DataFrame: python lookup_df = pd.DataFrame({'Grade': [1, 2, 3, 4, 5], 'Letter': ['A', 'B', 'C', 'D', 'F']}) df = df.merge(lookup_df, how='left', on='Grade') Explanation: – We have another DataFrame `lookup_df` that contains a mapping between numerical grades and letter grades. – We use the `merge()` function to join `df` and `lookup_df` based on the common column ‘Grade’. – The resulting DataFrame `df` will have a new column ‘Letter’ containing the corresponding letter grades based on the lookup. I hope these examples help you understand how to vectorize conditional assignment in a pandas DataFrame. Let me know if you have any further questions!

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Python Conditional Assignment

When you want to assign a value to a variable based on some condition, like if the condition is true then assign a value to the variable, else assign some other value to the variable, then you can use the conditional assignment operator.

In this tutorial, we will look at different ways to assign values to a variable based on some condition.

1. Using Ternary Operator

The ternary operator is very special operator in Python, it is used to assign a value to a variable based on some condition.

It goes like this:

Here, the value of variable will be value_if_true if the condition is true, else it will be value_if_false .

Let's see a code snippet to understand it better.

You can see we have conditionally assigned a value to variable c based on the condition a > b .

2. Using if-else statement

if-else statements are the core part of any programming language, they are used to execute a block of code based on some condition.

Using an if-else statement, we can assign a value to a variable based on the condition we provide.

Here is an example of replacing the above code snippet with the if-else statement.

3. Using Logical Short Circuit Evaluation

Logical short circuit evaluation is another way using which you can assign a value to a variable conditionally.

The format of logical short circuit evaluation is:

It looks similar to ternary operator, but it is not. Here the condition and value_if_true performs logical AND operation, if both are true then the value of variable will be value_if_true , or else it will be value_if_false .

Let's see an example:

But if we make condition True but value_if_true False (or 0 or None), then the value of variable will be value_if_false .

So, you can see that the value of c is 20 even though the condition a < b is True .

So, you should be careful while using logical short circuit evaluation.

While working with lists , we often need to check if a list is empty or not, and if it is empty then we need to assign some default value to it.

Let's see how we can do it using conditional assignment.

Here, we have assigned a default value to my_list if it is empty.

Assign a value to a variable conditionally based on the presence of an element in a list.

Now you know 3 different ways to assign a value to a variable conditionally. Any of these methods can be used to assign a value when there is a condition.

The cleanest and fastest way to conditional value assignment is the ternary operator .

if-else statement is recommended to use when you have to execute a block of code based on some condition.

Happy coding! 😊

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COMMENTS

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

    5 ways to apply an IF condition in Pandas DataFrame June 25, 2022 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 Strings and lambda OR condition Applying an IF condition in Pandas DataFrame

  2. python

    1 Answer Sorted by: 7 You can use numpy.where (): import numpy as np original ["new"] = np.where (original ["col"].isin ( ["b", "x"]), 1, 99) print (original) # col new #0 a 99 #1 b 1 #2 c 99 Share Improve this answer Follow answered Apr 10, 2018 at 20:56 pault 42.3k 17 110 153 7

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

    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.

  4. 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

  5. Conditional Selection and Assignment With .loc in Pandas

    5 min read · 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.

  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. Ways to apply an if condition in Pandas DataFrame

    Practice. Generally on a Pandas DataFrame the if condition can be applied either column-wise, row-wise, or on an individual cell basis. The further document illustrates each of these with examples. First of all we shall create the following DataFrame : python. import pandas as pd. df = pd.DataFrame ( {. 'Product': ['Umbrella', 'Mattress ...

  8. 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 ...

  9. 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'],

  10. Efficient Conditional Logic on Pandas DataFrames

    Pandas .apply (), straightforward, is used to apply a function along an axis of the DataFrame or on values of Series. For example, if we have a function f that sum an iterable of numbers (i.e. can be a list, np.array, tuple, etc.), and pass it to a dataframe like below, we will be summing across a row: def f (numbers):

  11. 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.

  12. 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)

  13. DataFrames with Conditionals

    DataFrames with Conditionals Watch on The use of conditionals allows us to select a subset of rows based on the value in each row. Writing a conditional to select rows based on the data in a single column is straightforward and was used when we selected all of the courses taught by the Statistics department with the following code:

  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. Conditional selection in the DataFrame

    Conditional selection in the DataFrame | Pandas DataFrame Here, we are going to learn about the conditional selection in the Pandas DataFrame in Python, Selection Using multiple conditions, etc. Submitted by Sapna Deraje Radhakrishna, on January 06, 2020 Conditional selection in the DataFrame Consider the following example,

  16. python

    Sure! I can help you with that. Vectorizing the conditional assignment in a pandas DataFrame means applying a condition to a column in the DataFrame and assigning a value based on that condition. This can be done efficiently by using NumPy's vectorized operations.

  17. python

    1 Answer Sorted by: 12 try this (if you want to have your desired result set - checking b column): In [30]: df ['d'] = np.where (df.b.notnull (), df.b/ (df.b+df.c), df.c) In [31]: df Out [31]: b c d 0 2.0 NaN NaN 1 NaN 1.0 1.000000 2 4.0 2.0 0.666667 3 2.0 NaN NaN 4 NaN NaN NaN or this, checking c column:

  18. Python Conditional Assignment (in 3 Ways)

    When you want to assign a value to a variable based on some condition, like if the condition is true then assign a value to the variable, else assign some other value to the variable, then you can use the conditional assignment operator. In this tutorial, we will look at different ways to assign values to a variable based on some condition. 1.

  19. python

    Applying a conditional statement to all value of a dataframe Ask Question Asked 6 years, 9 months ago Modified 6 years, 9 months ago Viewed 1k times 2 I have a dataframe with several columns and indexes and I would like to replace each value by 1 if the value is positive, -1 else.