Adding Rows to a Pandas DataFrame: A Comprehensive Guide
Are you looking to add rows to a Pandas DataFrame? This task can seem daunting at first, but with the right approach, it is easier than you might think.
In this article, we’ll guide you through five different methods for adding rows to a Pandas DataFrame. Whether you prefer working with Pandas Series objects, Python dictionaries, or lists, there is a method to suit your needs. Let’s dive in!
Method 1: Using a Pandas Series Object
One way to add rows to a Pandas DataFrame is by using a Pandas Series object. This method involves creating a new Series object and then appending it to the DataFrame using the DataFrame.append() method.
Here’s an example:
import pandas as pd
# create the original DataFrame
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# create the new row to add to the DataFrame
new_row = pd.Series({'name': 'Charlie', 'age': 28})
# append the new row to the DataFrame
df = df.append(new_row, ignore_index=True)
print(df)
In this example, we create a new DataFrame with two rows. Next, we create a new row as a Pandas Series object with the values we want to add.
Finally, we append the new row to the original DataFrame using DataFrame.append() and set the ignore_index parameter to True to ensure the new row is given a new index value.
Method 2: Using a Python Dictionary
Another way to add rows to a Pandas DataFrame is by using a Python dictionary.
This method involves creating a new dictionary with the values you want to add, converting it to a DataFrame using pd.DataFrame(), and then appending it to the original DataFrame using DataFrame.append(). Here’s an example:
import pandas as pd
# create the original DataFrame
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# create the new row as a dictionary
new_row_dict = {'name': 'Charlie', 'age': 28}
# convert the dictionary to a DataFrame
new_row_df = pd.DataFrame([new_row_dict])
# append the new row to the DataFrame
df = df.append(new_row_df, ignore_index=True)
print(df)
In this example, we create a new dictionary with the values we want to add to the DataFrame. Next, we convert the dictionary to a DataFrame using pd.DataFrame().
Finally, we append the new row DataFrame to the original DataFrame using DataFrame.append() with ignore_index set to True.
Method 3: Using a Python List
A third method for adding rows to a Pandas DataFrame is by using a Python list.
This method involves creating a new list with the values you want to add, and then using DataFrame.loc[] to append the list as a new row to the DataFrame. Here’s an example:
import pandas as pd
# create the original DataFrame
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# create the new row as a list
new_row_list = ['Charlie', 28]
# append the new row to the DataFrame
df.loc[len(df)] = new_row_list
print(df)
In this example, we create a new list with the values we want to add to the DataFrame. We then use DataFrame.loc[] to insert the new row at the end of the DataFrame.
Notice that we use len(df) to get the index value for the new row so that it is appended after the last row.
Method 4: Appending Rows with DataFrame.append()
Our fourth method for adding rows to a Pandas DataFrame utilizes DataFrame.append() again, but this time with a DataFrame instead of a Series object or a Python dictionary.
This method is useful if you have multiple rows to add at once. Here’s an example:
import pandas as pd
# create the original DataFrame
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# create the new rows as a DataFrame
new_rows_df = pd.DataFrame({'name': ['Charlie', 'Dave'], 'age': [28, 32]})
# append the new rows to the DataFrame
df = df.append(new_rows_df, ignore_index=True)
print(df)
In this example, we create a new DataFrame with two rows to add to the original DataFrame. We then append the new DataFrame to the original using DataFrame.append() with ignore_index set to True.
Method 5: Replacing Rows with DataFrame.iloc[]
Our final method for adding rows to a Pandas DataFrame involves replacing an existing row using DataFrame.iloc[]. This method is useful if you want to change the values in an existing row.
Here’s an example:
import pandas as pd
# create the original DataFrame
df = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie'], 'age': [25, 30, 28]})
# create the new row to replace an existing row
new_row = {'name': 'Dave', 'age': 32}
# replace the third row with the new row
df.iloc[2] = new_row
print(df)
In this example, we create a new dictionary with the values we want to use to replace an existing row. We then use DataFrame.iloc[] to replace the values in the third row of the DataFrame with the values from the new dictionary.
Creating a Sample Pandas DataFrame
Now that we’ve explored five different methods for adding rows to a Pandas DataFrame, let’s create a sample DataFrame to work with.
import pandas as pd
# create a list of dictionaries
data = [
{'name': 'Alice', 'age': 25},
{'name': 'Bob', 'age': 30},
{'name': 'Charlie', 'age': 28},
{'name': 'Dave', 'age': 32},
{'name': 'Ellen', 'age': 27}
]
# create the DataFrame
df = pd.DataFrame(data)
# print the DataFrame
print(df)
In this example, we create a list of dictionaries with the values we want to use in our DataFrame. We then use pd.DataFrame() to create the DataFrame and print() to display it.
Conclusion
Adding rows to a Pandas DataFrame is an essential skill for working with data in Python. We hope that this article has provided you with an informative guide on how to add rows to a DataFrame using five different methods.
Whether you prefer working with Pandas Series objects, Python dictionaries, or lists, there is a method to suit your needs. Happy coding!