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## Popular Tutorials

Popular examples, reference materials, learn python interactively, pandas assign().

The assign() method in Pandas is used to create a new column in a DataFrame or modify an existing one.

## assign() Syntax

The syntax of the assign() method in Pandas is:

## assign() Argument

The assign() method takes the following argument:

- **kwargs : the column names and their corresponding values or functions.

## assign() Return Value

The assign() method returns a new DataFrame with the assigned columns. The original DataFrame remains unchanged.

## Example 1: Basic Column Assignment

In this example, we assigned column B to df and displayed the resulting DataFrame.

## Example 2: Assignment Using Functions

We can assign columns based on the values in the existing DataFrame using functions.

In this example, we assigned values to the new column B that are double the values in column A using lambda function.

## Example 3: Multiple Column Assignments

We can assign multiple columns at once using the assign() method.

## Example 4: Chaining Assignments

We can chain the assign() method to assign multiple columns.

In this example, we first assigned column B . In the next assign() call, we used the newly created B and existing A to assign column C .

## Assign Values to Multiple Columns in a pandas DataFrame

df.assign allows you to assign values to multiple new columns in one line of code.

In the code above, I first created col3 then use col3 to create col4 in one line of code.

## python – Pandas: Assigning multiple *new* columns simultaneously

Sure! Here are 8 examples of assigning multiple new columns simultaneously in pandas, along with a step-by-step explanation of each code. Example 1: import pandas as pd data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]} df = pd.DataFrame(data) df[['C', 'D']] = [10, 20] Step-by-step explanation: 1. Import the pandas library. 2. Create a dictionary `data` containing two columns ‘A’ and ‘B’ with some values. 3. Create a DataFrame `df` using the `pd.DataFrame()` function and passing the `data` dictionary as its argument. 4. Use the double square brackets `[[‘C’, ‘D’]]` to assign multiple new columns ‘C’ and ‘D’ to the DataFrame `df`. 5. Assign the values `[10, 20]` to the new columns ‘C’ and ‘D’. Example 2: df.loc[:, 'E':'F'] = [['foo', 'bar'], ['baz', 'qux'], ['spam', 'eggs'], ['hello', 'world']] Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous example), use the `.loc` accessor to select all rows `[:]` and columns ‘E’ to ‘F’ `[‘E’:’F’]`. 2. Assign a list `[[‘foo’, ‘bar’], [‘baz’, ‘qux’], [‘spam’, ‘eggs’], [‘hello’, ‘world’]]` to the selected columns ‘E’ and ‘F’. Example 3: df[['G', 'H']] = df[['A', 'B']] + 5 Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), use the double square brackets `[[‘G’, ‘H’]]` to assign multiple new columns ‘G’ and ‘H’ to the DataFrame `df`. 2. Use the double square brackets `[[‘A’, ‘B’]]` to select the existing columns ‘A’ and ‘B’. 3. Add 5 to each value in the selected columns ‘A’ and ‘B’, and assign the result to the new columns ‘G’ and ‘H’. Example 4: df[['I', 'J']] = df[['A', 'B']].apply(lambda x: x**2) Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), use the double square brackets `[[‘I’, ‘J’]]` to assign multiple new columns ‘I’ and ‘J’ to the DataFrame `df`. 2. Use the double square brackets `[[‘A’, ‘B’]]` to select the existing columns ‘A’ and ‘B’. 3. Apply a lambda function `lambda x: x**2` to each value in the selected columns ‘A’ and ‘B’, and assign the result to the new columns ‘I’ and ‘J’. Example 5: df.loc[:, 'K':'M'] = df.apply(lambda row: [row['A'] + row['B'], row['A'] - row['B'], row['A'] * row['B']], axis=1) Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), use the `.loc` accessor to select all rows `[:]` and columns ‘K’ to ‘M’ `[‘K’:’M’]`. 2. Apply a lambda function `lambda row: [row[‘A’] + row[‘B’], row[‘A’] – row[‘B’], row[‘A’] * row[‘B’]]` to each row in the DataFrame `df`. 3. The lambda function returns a list of values: the sum of columns ‘A’ and ‘B’, the difference of columns ‘A’ and ‘B’, and the product of columns ‘A’ and ‘B’. 4. Assign the list of values to the selected columns ‘K’, ‘L’, and ‘M’. Example 6: df[['N', 'O']] = df[['A', 'B']].applymap(str) + '_text' Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), use the double square brackets `[[‘N’, ‘O’]]` to assign multiple new columns ‘N’ and ‘O’ to the DataFrame `df`. 2. Use the double square brackets `[[‘A’, ‘B’]]` to select the existing columns ‘A’ and ‘B’. 3. Apply the `applymap()` function to each value in the selected columns ‘A’ and ‘B’, which converts them to strings. 4. Concatenate the converted values with the “_text” string, and assign the result to the new columns ‘N’ and ‘O’. Example 7: df['P'] = df['A'] * df['B'] df['Q'] = df['A'] + df['B'] Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), assign a new column ‘P’ to the DataFrame `df`, calculated as the product of columns ‘A’ and ‘B’. 2. Assign another new column ‘Q’ to the DataFrame `df`, calculated as the sum of columns ‘A’ and ‘B’. Example 8: df[['R', 'S']] = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=df.index) Step-by-step explanation: 1. Assuming the DataFrame `df` already exists (as created in the previous examples), use the double square brackets `[[‘R’, ‘S’]]` to assign multiple new columns ‘R’ and ‘S’ to the DataFrame `df`. 2. Create a new DataFrame in the format `pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=df.index)`. 3. The new DataFrame contains four rows and two columns with the values [1, 2], [3, 4], [5, 6], and [7, 8]. The row index is set to be the same as the index of the existing DataFrame `df`. 4. Assign the values from the new DataFrame to the new columns ‘R’ and ‘S’ in the existing DataFrame `df`.

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## How to Add Multiple Columns to Pandas DataFrame

You can use the following methods to add multiple columns to a pandas DataFrame:

## Method 1: Add Multiple Columns that Each Contain One Value

Method 2: add multiple columns that each contain multiple values.

The following examples show how to use each method with the following pandas DataFrame:

The following code shows how to add three new columns to the pandas DataFrame in which each new column only contains one value:

Notice that three new columns – new1 , new2 , and new3 – have been added to the DataFrame.

Also notice that each new column contains only one specific value.

The following code shows how to add three new columns to the pandas DataFrame in which each new column contains multiple values:

Also notice that each new column contains multiple values.

## Additional Resources

The following tutorials explain how to perform other common operations in pandas:

How to Sort by Multiple Columns in Pandas How to Check if Column Exists in Pandas How to Rename Columns in Pandas

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Multiple assignment in python: assign multiple values or the same value to multiple variables.

In Python, the = operator is used to assign values to variables.

You can assign values to multiple variables in one line.

## Assign multiple values to multiple variables

Assign the same value to multiple variables.

You can assign multiple values to multiple variables by separating them with commas , .

You can assign values to more than three variables, and it is also possible to assign values of different data types to those variables.

When only one variable is on the left side, values on the right side are assigned as a tuple to that variable.

If the number of variables on the left does not match the number of values on the right, a ValueError occurs. You can assign the remaining values as a list by prefixing the variable name with * .

For more information on using * and assigning elements of a tuple and list to multiple variables, see the following article.

- Unpack a tuple and list in Python

You can also swap the values of multiple variables in the same way. See the following article for details:

- Swap values in a list or values of variables in Python

You can assign the same value to multiple variables by using = consecutively.

For example, this is useful when initializing multiple variables with the same value.

After assigning the same value, you can assign a different value to one of these variables. As described later, be cautious when assigning mutable objects such as list and dict .

You can apply the same method when assigning the same value to three or more variables.

Be careful when assigning mutable objects such as list and dict .

If you use = consecutively, the same object is assigned to all variables. Therefore, if you change the value of an element or add a new element in one variable, the changes will be reflected in the others as well.

If you want to handle mutable objects separately, you need to assign them individually.

after c = []; d = [] , c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d .) 3. Data model — Python 3.11.3 documentation

You can also use copy() or deepcopy() from the copy module to make shallow and deep copies. See the following article.

- Shallow and deep copy in Python: copy(), deepcopy()

## Related Categories

Related articles.

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- numpy.where(): Manipulate elements depending on conditions
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## Add Multiple Columns to pandas DataFrame in Python (Example)

In this tutorial, I’ll illustrate how to append multiple new variables to a pandas DataFrame in the Python programming language .

The content is structured as follows:

So let’s take a look at some Python codes in action:

## Example Data & Add-On Libraries

In order to use the functions of the pandas library, we first have to load pandas :

The following data is used as basement for this Python programming tutorial:

Table 1 shows that our example data consists of four rows and three columns.

Next, we have to create several list objects that we will add as new columns to our pandas DataFrame later on.

Note that our two lists have the same length as the number of rows of our data set.

## Example: Append Multiple Columns to pandas DataFrame

In this example, I’ll demonstrate how to combine multiple new columns with an existing pandas DataFrame in one line of code.

Consider the following python syntax:

By running the previous code, we have created Table 2, i.e. a new pandas DataFrame containing a union of our example data set plus our two list objects.

## Video & Further Resources

In case you need further info on how to merge and join new columns to a pandas DataFrame, you could watch the following video on my YouTube channel. I explain the examples of this article in the video instruction.

In addition, you may want to read the related tutorials on my website. You can find a selection of tutorials on related topics such as counting and descriptive statistics below:

- Types of Joins for pandas DataFrames in Python
- Add Column from Another pandas DataFrame
- rbind & cbind pandas DataFrame in Python
- Combine pandas DataFrames Vertically & Horizontally
- Merge List of pandas DataFrames in Python
- Merge pandas DataFrames based on Particular Column
- Merge pandas DataFrames based on Index
- Merge Multiple pandas DataFrames in Python
- Merge Two pandas DataFrames in Python
- Combine pandas DataFrames with Different Column Names
- Combine pandas DataFrames with Same Column Names
- Append Multiple pandas DataFrames in Python
- Append pandas DataFrame in Python
- Count Rows & Columns of pandas DataFrame in Python
- Sum of Columns & Rows of pandas DataFrame in Python
- Change Order of Columns in pandas DataFrame in Python
- Add Column to pandas DataFrame in Python
- pandas DataFrame Operations in Python
- DataFrame Manipulation Using pandas in Python
- Introduction to the pandas Library in Python
- Introduction to Python

In this Python tutorial you have learned how to add and concatenate several new variables to a pandas DataFrame . If you have any additional comments and/or questions, please let me know in the comments. Besides that, don’t forget to subscribe to my email newsletter in order to receive updates on new articles.

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## Related Tutorials

## Summary Statistics of pandas DataFrame in Python (4 Examples)

## Combine Two Text Columns of pandas DataFrame in Python (Example)

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Home » Python » Python Programs

- Pandas Assigning multiple new columns simultaneously

Learn, how can we assign multiple new columns simultaneously in Python pandas? Submitted by Pranit Sharma , on October 03, 2022

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 with a column containing labels for each row. We are also given a dictionary with keys equal to possible labels and values equal to 2-tuples of information related to that label. We aim to create new rows for each value of the tuple in that dictionary.

## Assigning multiple new columns simultaneously

For this purpose, we will first define a function where we will calculate some mathematical operations and return these operations to store them in the columns of the DataFrame to add new rows.

Let us understand with the help of an example,

## Python program to assign multiple new columns simultaneously

The output of the above program is:

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- 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
- Calculate new column as the mean of other columns in pandas
- Slice Pandas DataFrame by Row
- 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
- 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
- 'Anti-merge' in Pandas
- Pandas dataframe select rows where a list-column contains any of a list of strings
- Order columns of a pandas dataframe according to the values in a row
- 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
- Dynamically filtering a pandas dataframe
- 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
- How to suppress matplotlib warning?
- Filter/Select rows of pandas dataframe by timestamp column
- How to fix pandas not reading first column from csv file?
- How to save image created with 'pandas.DataFrame.plot'?
- Pandas: Assign an index to each group identified by groupby
- Why does my Pandas DataFrame not display new order using `sort_values`?
- How can I group by month from a date field using Python and Pandas?
- Using regex matched groups in pandas dataframe replace function
- Pandas DataFrame concat / update ('upsert')?
- How to Pandas fillna() with mode of column?
- Determining when a column value changes in pandas dataframe
- Count number of words per row
- Reduce precision pandas timestamp dataframe
- Pandas: Reset index is not taking effect
- Combine duplicated columns within a DataFrame
- How to remove rows with null values from kth column onward?
- 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
- How to subtract a single value from column of pandas DataFrame?
- map() function inserting NaN, possible to return original values instead?
- Pandas: reset_index() after groupby.value_counts()
- Pandas scatter plotting datetime
- How can I split a column of tuples in a Pandas dataframe?
- Binning a column with pandas
- Pandas: Conditional creation of a series/dataframe column
- What is the difference between size and count in pandas?
- float64 with pandas to_csv
- Iterating through columns and subtracting with the Last Column in pd.dataframe
- String concatenation of two pandas columns
- Convert timedelta64[ns] column to seconds in Pandas DataFrame
- Fast punctuation removal with pandas
- 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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## Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python variables - assign multiple values, many values to multiple variables.

Python allows you to assign values to multiple variables in one line:

Note: Make sure the number of variables matches the number of values, or else you will get an error.

## One Value to Multiple Variables

And you can assign the same value to multiple variables in one line:

## Unpack a Collection

If you have a collection of values in a list, tuple etc. Python allows you to extract the values into variables. This is called unpacking .

Unpack a list:

Learn more about unpacking in our Unpack Tuples Chapter.

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- Python | Pandas dataframe.clip()
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- Pandas DataFrame get_value() | Retrieve Value from a Cell
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- Python | Pandas dataframe.idxmax()
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- Python | Pandas dataframe.isna()
- Python | Pandas dataframe.get()
- Python | Pandas dataframe.floordiv()
- Python | Pandas dataframe.asfreq()
- Python | Pandas dataframe.first_valid_index()
- Pandas DataFrame interpolate() Method | Pandas Method
- Python | Pandas dataframe.eq()
- Python | Pandas dataframe.diff()
- Python | Pandas dataframe.corrwith()
- Pandas DataFrame corr() Method
- Python | Pandas dataframe.equals()
- Python | Pandas dataframe.count()

## Return multiple columns using Pandas apply() method

Objects passed to the pandas.apply() are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

## Creating Dataframe to return multiple columns using apply() method

Below are some programs which depict the use of pandas.DataFrame.apply()

Example 1:

Using a Numpy universal function (in this case the same as numpy.sqrt ()).

Example 2:

Using a reducing function on columns.

Example 3:

Using a reducing function on rows.

Example 4:

Returning a list-like will result in a Series using the lambda function .

Example 5:

Passing result_type=’expand’ will expand list-like results to columns of a Dataframe.

Example 6:

Returning a Series inside the function is similar to passing result_type=’expand’. The resulting column names will be the Series index.

Example 7:

Passing result_type=’broadcast’ will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcasted along the axis. The resulting column names will be the originals.

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## Set Pandas Conditional Column Based on Values of Another Column

- August 9, 2021 February 22, 2022

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

## 2 thoughts on “Set Pandas Conditional Column Based on Values of Another Column”

Thank you so much! Brilliantly explained!!!

Thanks Aisha!

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## Split Pandas column of lists into multiple columns

When working with pandas dataframe, you may find yourself in situations where you have a column with values as lists that you’d rather have in separate columns. In this tutorial, we will look at how to split a pandas dataframe column of lists into multiple columns with the help of some examples.

## How to create multiple columns from a pandas column of lists?

To split a pandas column of lists into multiple columns, create a new dataframe by applying the tolist() function to the column. The following is the syntax.

You can also pass the names of new columns resulting from the split as a list.

Let’s see it action with the help of an example. First, let’s create a dataframe with a column having a list of values for each row.

Now, let’s split the column “Values” into multiple columns, one for each value in the list.

Here, we didn’t pass any column names, hence the column names are given by default. Let’s give specific column names to each of the new columns.

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You may also want to concatenate the resulting dataframe from the split to the original dataframe. For this, use the pandas concat() function .

You may also want to drop the column “Values” now that it has been split into three columns.

## Split column of lists of variable lengths

What would happen if you use the above method on a column which has lists of variable lengths?

Let’s see for ourselves.

The column “Values” has lists of different lengths.

If the lists in the column are of different lengths, the resulting dataframe will have columns equal to the length of the largest list with NaNs in places where the function doesn’t find a list value.

Pandas series tolist() function

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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How to add multiple columns to pandas dataframe in one assignment Ask Question Asked 7 years, 5 months ago Modified 10 months ago Viewed 474k times 295 I'm trying to figure out how to add multiple columns to pandas simultaneously with Pandas. I would like to do this in one step rather than multiple repeated steps.

16 This question already has answers here : How to add multiple columns to pandas dataframe in one assignment (14 answers) Closed 3 years ago. I have follow simple DataFrame - df: 0 0 1 1 2 2 3 Once I try to create a new columns and assign some values for them, as example below: df ['col2', 'col3'] = [ (2,3), (2,3), (2,3)] I got following structure

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. Parameters: **kwargsdict of {str: callable or Series} The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns.

Add multiple columns to a data frame using Dataframe.assign () method Using DataFrame.assign () method, we can set column names as parameters and pass values as list to replace/create the columns. Python3 import pandas as pd students = [ ['jackma', 34, 'Sydeny', 'Australia'], ['Ritika', 30, 'Delhi', 'India'], ['Vansh', 31, 'Delhi', 'India'],

method in Pandas is used to create a new column in a DataFrame or modify an existing one. The syntax of the assign () method in Pandas is: method takes the following argument: : the column names and their corresponding values or functions. method returns a new DataFrame with the assigned columns. The original DataFrame remains unchanged.

Assign Values to Multiple Columns in a pandas DataFrame df.assign allows you to assign values to multiple new columns in one line of code. In the code above, I first created col3 then use col3 to create col4 in one line of code. Favorite

The assign() method either appends a new column or assigns new values to an existing column. pandas.DataFrame.assign — pandas 2.0.3 documentation; You can specify the column name and its value using the keyword argument structure, column_name=value. If the column name exists, the method assigns the value to it. If the column name is new, it ...

3. Create a DataFrame `df` using the `pd.DataFrame()` function and passing the `data` dictionary as its argument. 4. Use the double square brackets `[['C', 'D']]` to assign multiple new columns 'C' and 'D' to the DataFrame `df`. 5. Assign the values `[10, 20]` to the new columns 'C' and 'D'. Example 2:

The assign () method can be used to add new columns to a pandas DataFrame. This method uses the following basic syntax: df.assign(new_column = values) It's important to note that this method will only output the new DataFrame to the console, but it won't actually modify the original DataFrame.

Method 1: Add Multiple Columns that Each Contain One Value df [ ['new1', 'new2', 'new3']] = pd.DataFrame( [ [4, 'hey', np.nan]], index=df.index) Method 2: Add Multiple Columns that Each Contain Multiple Values df ['new1'] = [1, 5, 5, 4, 3, 6] df ['new2'] = ['hi', 'hey', 'hey', 'hey', 'hello', 'yo'] df ['new3'] = [12, 4, 4, 3, 6, 7]

For more information on using * and assigning elements of a tuple and list to multiple variables, see the following article.. Unpack a tuple and list in Python; You can also swap the values of multiple variables in the same way. See the following article for details:

In this example, I'll demonstrate how to combine multiple new columns with an existing pandas DataFrame in one line of code. Consider the following python syntax: data_new = data. copy ( ) # Create copy of DataFrame data_new [ "new1" ] , data_new [ "new2" ] = [ new1 , new2 ] # Add multiple columns print ( data_new ) # Print updated pandas ...

Using pandas append () method within for loop Selecting columns by list where columns are subset of list Counting the frequency of words in a pandas dataframe Create multiple dataframes in loop Pandas dataframe str.contains () AND operation How to convert pandas series to tuple of index and value? All Python Pandas Programs

Must write the names of the columns needed in the conditions again as the lambda function now refers to x(e.g. x["Sales"]). We can't use the global variables defined at the begining, because ...

Python allows you to assign values to multiple variables in one line: Example Get your own Python Server x, y, z = "Orange", "Banana", "Cherry" print(x) print(y) print(z) Try it Yourself » Note: Make sure the number of variables matches the number of values, or else you will get an error. One Value to Multiple Variables

Return multiple columns using Pandas apply () method. Objects passed to the pandas.apply () are Series objects whose index is either the DataFrame's index (axis=0) or the DataFrame's columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function.

We'll begin by import pandas and loading a dataframe using the .from_dict () method: import pandas as pd df = pd.DataFrame.from_dict ( { 'Name': [ 'Jane', 'Melissa', 'John', 'Matt' ], 'Age': [ 23, 45, 35, 64 ], 'Birth City': [ 'London', 'Paris', 'Toronto', 'Atlanta' ], 'Gender': [ 'F', 'F', 'M', 'M' ] } ) print (df)

1 Is there an elegant way to assign values based on multiple columns in a dataframe in pandas? Let's say I have a dataframe with 2 columns: FruitType and Color. import pandas as pd df = pd.DataFrame ( {'FruitType': ['apple', 'banana','kiwi','orange','loquat'], 'Color': ['red_black','yellow','greenish_yellow', 'orangered','orangeyellow']})

To split a pandas column of lists into multiple columns, create a new dataframe by applying the tolist () function to the column. The following is the syntax. import pandas as pd # assuming 'Col' is the column you want to split df.DataFrame(df['Col'].to_list(), columns = ['c1', 'c2', 'c3'])

7 Answers Sorted by: 73 numpy.select This is a perfect case for np.select where we can create a column based on multiple conditions and it's a readable method when there are more conditions: