> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # Combine Multiple columns into a single one in Pandas - URL: https://datascientyst.com/combine-multiple-columns-into-single-one-in-pandas/ - Published: 2021-11-03T08:14:59.000Z - Updated: 2025-12-06T10:12:43.000Z - Author: John D K - Tags: Column In this short guide, you'll see how to combine multiple columns into a single one in Pandas. Here you can find the short answer: **(1) String concatenation** ```python df['Magnitude Type'] + ', ' + df['Type'] ``` **(2) Using methods `agg` and `join`** ```python df[['Date', 'Time']].T.agg(','.join) ``` **(3) Using lambda and join** ```python df[['Date', 'Time']].agg(lambda x: ','.join(x.values), axis=1).T ``` So let's see several useful examples on how to combine several columns into one with Pandas. Suppose you have data like: | Date | Time | Depth | Magnitude Type | Type | Magnitude | | ---------- | -------- | ----- | -------------- | ---------- | --------- | | 01/02/1965 | 13:44:18 | 131.6 | MW | Earthquake | 6.0 | | 01/04/1965 | 11:29:49 | 80.0 | MW | Earthquake | 5.8 | | 01/05/1965 | 18:05:58 | 20.0 | MW | Earthquake | 6.2 | | 01/08/1965 | 18:49:43 | 15.0 | MW | Earthquake | 5.8 | | 01/09/1965 | 13:32:50 | 15.0 | MW | Earthquake | 5.8 | ## 1: Combine multiple columns using string concatenation Let's start with most simple example - to combine two string columns into a single one separated by a comma: ```python df['Magnitude Type'] + ', ' + df['Type'] ``` result will be: ``` 0 MW, Earthquake 1 MW, Earthquake 2 MW, Earthquake 3 MW, Earthquake 4 MW, Earthquake ``` What if one of the columns is not a string? Then you will get error like: > TypeError: can only concatenate str (not "float") to str To avoid this error you can convert the column by using method `.astype(str)`: ```python df['Magnitude Type'] + ', ' + df['Magnitude'].astype(str) ``` result: ``` 0 MW, 6.0 1 MW, 5.8 2 MW, 6.2 3 MW, 5.8 4 MW, 5.8 ``` ## 2: Combine date and time columns into DateTime column What if you have separate columns for the date and the time. You can concatenate them into a single one by using string concatenation and conversion to datetime: ```python pd.to_datetime(df['Date'] + ' ' + df['Time'], errors='ignore') ``` In case of missing or incorrect data we will need to add parameter: `errors='ignore'` in order to avoid error: > ParserError: Unknown string format: 1975-02-23T02:58:41.000Z 1975-02-23T02:58:41.000Z ![](https://datascientyst.com/content/images/2021/11/combine-multiple-columns-into-single-one-in-pandas.png) ## 3: Combine multiple columns with agg and join Another option to concatenate multiple columns is by using two Pandas methods: - `agg` - `join` ```python df[['Date', 'Time']].T.agg(','.join) ``` result: ``` 0 01/02/1965,13:44:18 1 01/04/1965,11:29:49 2 01/05/1965,18:05:58 3 01/08/1965,18:49:43 ``` This one might be a bit slower than the first one. ## 4: Combine multiple columns with lambda and join You can use lambda expressions in order to concatenate multiple columns. The advantages of this method are several: - you can have condition on your input - like filter - output can be customised - better control on dtypes To combine columns date and time we can do: ```python df[['Date', 'Time']].agg(lambda x: ','.join(x.values), axis=1).T ``` In the next section you can find how we can use this option in order to combine columns with the same name. ## 5: Combine columns which have the same name Finally let's combine all columns which have exactly the same name in a Pandas DataFrame. First let's create duplicate columns by: ```python df.columns = ['Date', 'Date', 'Depth', 'Magnitude Type', 'Type', 'Magnitude'] df ``` A general solution which **concatenates columns with duplicate names can be:** ```python df.groupby(df.columns, axis=1).agg(lambda x: x.apply(lambda y: ','.join([str(l) for l in y if str(l) != "nan"]), axis=1)) ``` This will result into: | Date | Depth | Magnitude | Magnitude Type | Type | | ------------------- | ----- | --------- | -------------- | ---------- | | 01/02/1965,13:44:18 | 131.6 | 6.0 | MW | Earthquake | | 01/04/1965,11:29:49 | 80.0 | 5.8 | MW | Earthquake | | 01/05/1965,18:05:58 | 20.0 | 6.2 | MW | Earthquake | | 01/08/1965,18:49:43 | 15.0 | 5.8 | MW | Earthquake | | 01/09/1965,13:32:50 | 15.0 | 5.8 | MW | Earthquake | How does it work? First is grouping the columns which share the same name: ```python for i in df.groupby(df.columns, axis=1): print(i) ``` result: ``` ('Date', Date Date 0 01/02/1965 13:44:18 1 01/04/1965 11:29:49 2 01/05/1965 18:05:58 3 01/08/1965 18:49:43 4 01/09/1965 13:32:50 ... ... ... 23407 12/28/2016 08:22:12 23408 12/28/2016 09:13:47 23409 12/28/2016 12:38:51 23410 12/29/2016 22:30:19 23411 12/30/2016 20:08:28 [23412 rows x 2 columns]) ('Depth', Depth 0 131.60 1 80.00 2 20.00 3 15.00 4 15.00 ... ... 23407 12.30 23408 8.80 ``` Then it's combining their values: ```python df.groupby(df.columns, axis=1).apply(lambda x: x.values) ``` result: ``` Date [[01/02/1965, 13:44:18], [01/04/1965, 11:29:49... Depth [[131.6], [80.0], [20.0], [15.0], [15.0], [35.... Magnitude [[6.0], [5.8], [6.2], [5.8], [5.8], [6.7], [5.... Magnitude Type [[MW], [MW], [MW], [MW], [MW], [MW], [MW], [MW... Type [[Earthquake], [Earthquake], [Earthquake], [Ea... ``` Finally there is prevention of errors in case of bad values like NaN, missing values, None, different formats etc. ## Resources - [Notebook](https://github.com/softhints/Pandas-Tutorials/blob/master/column/combine-multiple-columns-into-single-one-in-pandas.ipynb?ref=datascientyst.com) - [Working with text data](https://pandas.pydata.org/docs/user%5Fguide/text.html?ref=datascientyst.com) - [pandas.core.groupby.GroupBy.agg](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.agg.html?highlight=agg&ref=datascientyst.com#pandas.core.groupby.GroupBy.agg) - [pandas.core.groupby.GroupBy.apply](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.apply.html?highlight=apply&ref=datascientyst.com#pandas.core.groupby.GroupBy.apply)