How to add a new row to a Pandas DataFrame?

 To add a new row to a Pandas DataFrame, you can use one of the following methods:

Method 1: Append a Dictionary to the DataFrame

You can append a dictionary to the DataFrame using the append() method. Here is an example:

python
import pandas as pd

# create a DataFrame
df = pd.DataFrame({'Name': ['John', 'Alice', 'Bob'],
                   'Age': [25, 30, 35]})

# create a dictionary for the new row
new_row = {'Name': 'Dave', 'Age': 40}

# append the dictionary to the DataFrame
df = df.append(new_row, ignore_index=True)

# print the updated DataFrame
print(df)

In this example, we first create a DataFrame with three rows. We then create a dictionary new_row for the new row we want to add. We use the append() method to add the new row to the DataFrame. The ignore_index=True parameter tells Pandas to reset the index of the DataFrame after appending the new row.

Method 2: Append a Series to the DataFrame

You can also append a Pandas Series to the DataFrame using the loc[] method. Here is an example:

python
import pandas as pd

# create a DataFrame
df = pd.DataFrame({'Name': ['John', 'Alice', 'Bob'],
                   'Age': [25, 30, 35]})

# create a Pandas Series for the new row
new_row = pd.Series(['Dave', 40], index=df.columns)

# append the Series to the DataFrame
df = df.append(new_row, ignore_index=True)

# print the updated DataFrame
print(df)

In this example, we first create a DataFrame with three rows. We then create a Pandas Series new_row for the new row we want to add. We use the loc[] method to append the new row to the DataFrame. The ignore_index=True parameter tells Pandas to reset the index of the DataFrame after appending the new row.

Both methods should produce the same output DataFrame:

   Name  Age
0  John   25
1  Alice  30
2  Bob    35
3  Dave   40