Pandas provide a convenient way to handle data and its transformation. How to react to a students panic attack in an oral exam? Just need to pull each subject in a new row with its corresponding marks and copy all the other column contents as it is. Creating columns Renaming columns Selecting rows and columns Filtering rows by condition Manipulating strings Working with data types Encoding data Extracting data from lists Working with time series data Handling missing values Using aggregation functions Using cumulative functions Random sampling Merging DataFrames Styling DataFrames Get a list from Pandas DataFrame column headers, Retrieve the current price of a ERC20 token from uniswap v2 router using web3js. Each item in destination_rows has the index to the original row which will be the source of the new data. You may also want to concatenate the resulting dataframe from the split to the original dataframe. Summary: expand a column of lists into multiple rows in pandas; Matched Content: To split a pandas column of lists into multiple columns, create a new dataframe by applying the tolist() function to the column. Create a dataframe first with dict of lists. Pandas DataFrame columns are Pandas Series when you pull them out, which you can then call x.tolist() on to turn them into a Python list. Column (s) to explode. Power Query to Split Values into Columns Let's see how can we convert a column to row name/index in Pandas. Convert each string of names to a list and use Pandas explode() function to split the list by each element and create a new row for each of them. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Statology is a site that makes learning statistics easy by explaining topics in simple and straightforward ways. Next well look at turning this into a pandas accessor. Providing youre running version 1.3.0 or greater of Pandas, you can also explode multiple list columns into dataframe rows. In addition, the ordering of rows in the We are able to conduct this transformation in one line through the use of a list comprehension. Python df_melt = df.assign (names=df.names.str.split (",")) print(df_melt) Output: Now, split names column list values (columns with individual list values are created). How to get the number of rows in a Dataframe? How to expand pandas Dataframe column of DICT? You can use the following basic syntax to split a column of lists into multiple columns in a pandas DataFrame: #split column of lists into two new columns split = pd.DataFrame(df ['my_column'].to_list(), columns = ['new1', 'new2']) #join split columns back to original DataFrame df = pd.concat( [df, split], axis=1) This gives us the same dataframe as the one we got from the explode method but with a sequential index. How to expand rows from list data available in column? Filters rows where the column is missing. A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. 1 How to expand pandas column into multiple rows? flatten (). The result dtype of the subset rows will be object. You need to flatten the data and turn it into a CSV or analyze the list, youll need to read each item and most likely want to preserve other fields on the row. Yes, I knew there had to be something. import pandas as pd df = pd.DataFrame ( {'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/11'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], result in a np.nan for that row. Method 1: Using Pandas melt function First, convert each string of names to a list. How to create an individual column for each of these? A groupby operation involves some combination of splitting the object, applying a function, and combining the results. Now is the time for a nested list comprehension to expand the list values into a flat array. Lets see how can we convert a column to row name/index in Pandas. Can the Spiritual Weapon spell be used as cover? If you import this file, pandas will magically add a my_accessor attribute to all DataFrames, with the accessor functions exposed as methods of that attribute. DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=