How to add row to dataframe python
How to add row to dataframe python
How to Add or Insert Row to Pandas DataFrame?
Pandas DataFrame – Add or Insert Row
To append or add a row to DataFrame, create the new row as Series and use DataFrame.append() method.
In this tutorial, we shall learn how to append a row to an existing DataFrame, with the help of illustrative example programs.
Syntax – append()
Following is the syntax of DataFrame.appen() function.
where the resulting DataFrame contains new_row added to mydataframe.
append() is immutable. It does not change the DataFrame, but returns a new DataFrame with the row appended.
Example 1: Add Row to DataFrame
In this example, we will create a DataFrame and append a new row to this DataFrame. The new row is initialized as a Python Dictionary and append() function is used to append the row to the dataframe.
The append() method returns the dataframe with the newly added row.
Python Program
Output
Run the above Python program, and you shall see the original dataframe, and the dataframe appended with the new row.
Example 2: Add Row to Pandas DataFrame (ignoreIndex = False)
Python Program
Output
As the error message says, we need to either provide the parameter ignore_index=True or append the row, that is Series with a name.
Python Program
Output
Summary
In this Pandas Tutorial, we have used append() function to add a row to Pandas DataFrame.
5 Easy Ways to Add Rows to a Pandas Dataframe
In this Python tutorial, we are going to discuss the top five ways to add or insert one or multiple rows to the pandas DataFrame object. So, let’s get started with our discussion.
Methods to Add Rows to a Pandas Dataframe
Let’s first create a sample pandas DataFrame object to start with and then we will keep adding one or multiple rows to it using the following methods.
Output:
Method #1
Add a pandas Series object as a row to the existing pandas DataFrame object.
Output:
Method #2
Add a Python dictionary as a row to the existing pandas DataFrame object.
Output:
NOTE: Please set the ignore_index parameter of the DataFrame.append() function to True while passing a Python dictionary or a pandas Series otherwise, it will throw an error.
Method #3
Add a Python list object as a row to the existing pandas DataFrame object using DataFrame.loc[] method.
Output:
Method #4
Add the rows of one pandas DataFrame object to another pandas DataFrame object using the DataFrame.append() function.
Output:
Method #5
Add a row to the existing pandas DataFrame object at a specific index position using DataFrame.iloc[] method.
Output:
NOTE: Kindly take care while using the DataFrame.iloc[] method, as it replaces the existing row at that index position with the new row.
Conclusion
In this tutorial, We have learned the top five methods to add or insert one or multiple rows to an existing pandas DataFrame object. Hope you have understood the things discussed above well and are ready to use these methods in your own data analysis project. Thanks for reading! Stay tuned with us for more exciting learning resources on Python programming.
Add row to Dataframe Python Pandas
In this Python Pandas tutorial, will learn how to add a row to Dataframe in Python using Pandas. Also, we will cover these topics.
Add row to DataFrame Python Pandas
Let’s have a look and understand these methods
By using DataFrame.append() method
The Syntax of this method is here
Example:
Let’s take an example and check how to add a row to DataFrame
In the above code first, we have created a list of tuples ‘new_val’ and then declare a dataframe object ‘result’ in which we have assigned the column names. Now we want to add a row in an existing dataframe to do this we have used DataFrame.append() method and pass the dictionary as a new row of DataFrame.
Here is the execution of the following given code
By using iloc() method
In Python, the iloc() method is used for selecting specific rows. It accepts only integer values and it helps the user to select a value that belongs to a specific row and column.
Syntax:
Here is the Syntax of the dataframe. iloc() method
Source Code:
Here is the Output of the following given code
By using Concatenate method
In this program, we will add multiple rows by using pandas.Concat() method. In Pandas DataFrame we can easily be combining series or dataframe with various datasets by using Pandas.Concat() method.
Syntax:
Here is the Syntax of Pandas.Concat() method
Example:
In the above code first, we have created a dictionary ‘Employee_info1’ and then declare a dataframe object in which we have passed the dictionary as an argument. Similarly, we have created another dictionary ‘Employee_info2’.
Now we want to concatenate two different Dataframe and store the result into ‘new_val’. Once you will print the ‘new_val’ then the output will display new rows in the DataFrame.
Here is the Screenshot of the following given code
Add rows to DataFrame Pandas in loop
Source Code:
In the above code first, we create a variable and assign a column name in the list.
Here is the implementation of the following given code
Adding new row to DataFrame in Pandas
Source Code:
In the above code first, we have created a dictionary ‘new_dictionary’ and then declare a dataframe object ‘result’ in which we have assigned the dictionary name.
Now we want to add a row in an existing dataframe to do this we have used DataFrame.append() method and pass the dictionary as a new row of DataFrame.
You can refer to the below Screenshot
Adding new row to existing dataframe in Pandas
Example:
Here is the output of the following given code
Python Pandas add row to empty DataFrame
Source Code:
Here is the Screenshot of the following given code
Append rows to a DataFrame in Pandas
Source Code:
In the above code first, we have created a list of tuples ‘new_val’ and then declare a dataframe object ‘result’ in which we have assigned the column names.
Now we want to add a row in an existing dataframe to do this we have used DataFrame.append() method and pass the dictionary as a new row of DataFrame.
You can refer to the below Screenshot
Adding row to DataFrame Python Pandas groupby
Syntax:
Here is the Syntax of the groupby method
Example:
Here is the execution of the following given code
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In this Python Pandas tutorial, we have learned how to add a row to Dataframe in Python using Pandas. Also, we have covered these topics.
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From this question and others it seems that it is not recommended to use concat or append to build a pandas dataframe because it is recopying the whole dataframe each time.
My project involves retrieving a small amount of data every 30 seconds. This might run for a 3 day weekend, so someone could easily expect over 8000 rows to be created one row at a time. What would be the most efficient way to add rows to this dataframe?
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I used this answer’s df.loc[i] = [new_data] suggestion, but I have > 500,000 rows and that was very slow.
While the answers given are good for the OP’s question, I found it more efficient, when dealing with large numbers of rows up front (instead of the tricking in described by the OP) to use csvwriter to add data to an in memory CSV object, then finally use pandas.read_csv(csv) to generate the desired DataFrame output.
500,000 rows was 1000x faster and as the row count grows the speed improvement will only get larger ( the df.loc[1] = [data] will get a lot slower comparatively)
Hope this helps someone who need efficiency when dealing with more rows than the OP.
2 hours to 2 minutes!
Editing the chosen answer here since it was completely mistaken. What follows is an explanation of why you should not use setting with enlargement. «Setting with enlargement» is actually worse than append.
The tl;dr here is that there is no efficient way to do this with a DataFrame, so if you need speed you should use another data structure instead. See other answers for better solutions.
More on setting with enlargement
You can add rows to a DataFrame in-place using loc on a non-existent index, but that also performs a copy of all of the data (see this discussion). Here’s how it would look, from the Pandas documentation:
For something like the use case described, setting with enlargement actually takes 50% longer than append :
What about a longer DataFrame?
As with all profiling in data-oriented code, YMMV and you should test this for your use case. One characteristic of the copy-on-write behavior of append and «setting with enlargement» is that it will get slower and slower with large DataFrame s:
Building a 16k row DataFrame with this method takes 2.3x longer than 8k rows.
How to Add / Insert a Row into a Pandas DataFrame
In this tutorial, you’ll learn how to add (or insert) a row into a Pandas DataFrame. You’ll learn how to add a single row, multiple rows, and at specific positions. You’ll also learn how to add a row using a list, a Series, and a dictionary.
By the end of this tutorial, you’ll have learned:
Table of Contents
Loading a Sample Pandas DataFrame
To follow along with this tutorial line-by-line, you can copy the code below into your favourite code editor. If you have your own data to follow along with, feel free to do so (though your results will, of course, vary):
We have four records and three different columns, covering a person’s Name, Age, and Location.
Add a Row to a Pandas DataFrame
Add a Row to a Pandas DataFrame Using a Dictionary
Let’s say that we wanted to add a new row containing the following data: <'Name':'Jane', 'Age':25, 'Location':'Madrid'>.
We could simply write:
In the example above, we were able to add a new row to a DataFrame using a dictionary. Because we passed in a dictionary, we needed to pass in the ignore_index=True argument.
Add a Row to a Pandas DataFrame Using a List
As a fun aside: using iloc is more challenging since it requires that the index position already exist – meaning we would need to either add an empty row first or overwrite data.
Add a Row to a Pandas DataFrame Using a Series
Now let’s try to add the same row as shown above using a Pandas Series, that we can create using a Python list. We simply pass a list into the Series() function to convert the list to a Series. Let’s see how this works:
Insert a Row to a Pandas DataFrame at the Top
Adding a row to the top of a Pandas DataFrame is quite simple: we simply reverse the options you learned about above. By this, I mean to say we append the larger DataFrame to the new row.
However, we must first create a DataFrame. We can do this using the pd.DataFrame() class. Let’s take a look:
Insert a Row to a Pandas DataFrame at a Specific Index
Adding a row at a specific index is a bit different. As shown in the example of using lists, we need to use the loc accessor. However, inserting a row at a given index will only overwrite this. What we can do instead is pass in a value close to where we want to insert the new row.
For example, if we have current indices from 0-3 and we want to insert a new row at index 2, we can simply assign it using index 1.5. Let’s see how this works:
This, of course, makes a few assumptions:
Insert Multiple Rows in a Pandas DataFrame
Adding multiple rows to a Pandas DataFrame is the same process as adding a single row. However, it can actually be much faster, since we can simply pass in all the items at once. For example, if we add items using a dictionary, then we can simply add them as a list of dictionaries.
Let’s take a look at an example:
Conclusion
In this tutorial, you learned how to add and insert rows into a Pandas DataFrame. You learned a number of different methods to do this, including using dictionaries, lists, and Pandas Series. You also learned how to insert new rows at the top, bottom, and at a particular index. Finally, you also learned how to add multiple rows to a Pandas DataFrame at the same time.
Additional Resources
To learn more about related topics, check out the tutorials below: