How to Add Rows to Dataframe In Pandas?

To add rows to a dataframe in pandas, you can use the append() method. This method allows you to append a new row to the existing dataframe. You can create a new row as a dictionary or a list, and then use the append() method to add it to the dataframe. Just make sure that the new row has the same number of columns as the existing dataframe. The append() method returns a new dataframe with the added row, so you can assign it back to the original dataframe or a new variable.

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How to add multiple rows with different values to a dataframe in pandas?

You can add multiple rows with different values to a DataFrame in pandas by creating a dictionary or a list of dictionaries with the values you want to add, and then using the append() method to add them to the DataFrame. Here's an example:

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import pandas as pd

# Create a DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Define the rows you want to add
rows = [{'A': 1, 'B': 2, 'C': 3},
        {'A': 4, 'B': 5, 'C': 6},
        {'A': 7, 'B': 8, 'C': 9}]

# Add the rows to the DataFrame
for row in rows:
    df = df.append(row, ignore_index=True)

print(df)

This will output:

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4

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9

Alternatively, you can also use the concat() function to add multiple rows as a new DataFrame and then concatenate it with the existing DataFrame:

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new_df = pd.DataFrame(rows)
df = pd.concat([df, new_df], ignore_index=True)

print(df)

Both of these methods will add multiple rows with different values to a DataFrame in pandas.

What is the fastest method for adding rows to a large dataframe in pandas?

The fastest method for adding rows to a large dataframe in pandas is to use the append method or the concat function with a list of dataframes. This method avoids looping over each row to add it individually, which can be slow for large datasets. Here is an example using the append method:

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import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Create a new row to add
new_row = pd.Series({'A': 10, 'B': 20})

# Append the new row to the dataframe
df = df.append(new_row, ignore_index=True)

Alternatively, you can also use the concat function to concatenate a list of dataframes containing the new rows to add:

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# Create a list of dataframes with the new rows to add
new_rows = [pd.DataFrame({'A': [10], 'B': [20]}), pd.DataFrame({'A': [30], 'B': [40]})]

# Concatenate the dataframes
df = pd.concat([df] + new_rows, ignore_index=True)

Using these methods, you can efficiently add rows to a large dataframe in pandas without sacrificing performance.

How to add rows to a dataframe without modifying the original dataframe in pandas?

You can add rows to a DataFrame in pandas without modifying the original DataFrame by creating a new DataFrame that combines the original DataFrame with the new rows you want to add. Here is an example:

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import pandas as pd

# Create the original DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Create a new DataFrame with the rows you want to add
new_rows = pd.DataFrame({
    'A': [4, 5],
    'B': [7, 8]
})

# Concatenate the original DataFrame with the new rows
new_df = pd.concat([df, new_rows], ignore_index=True)

# Print the new DataFrame with the added rows
print(new_df)

In this example, we first create the original DataFrame df. Next, we create a new DataFrame new_rows with the rows we want to add. We then use the pd.concat() function to concatenate the original DataFrame with the new rows, setting ignore_index=True to reset the index of the new DataFrame. Finally, we print the new DataFrame with the added rows.