Python pandas: fill a dataframe row by row

Python pandas: fill a dataframe row by row

In pandas, you can fill a DataFrame row by row by using a combination of techniques. First, you need to create an empty DataFrame with the appropriate columns, and then you can add rows to it one by one. Here's a step-by-step guide on how to do this:

import pandas as pd # Create an empty DataFrame with the desired column names columns = ['Name', 'Age', 'City'] df = pd.DataFrame(columns=columns) # Define the data you want to add as rows data_to_add = [ ['Alice', 25, 'New York'], ['Bob', 30, 'Los Angeles'], ['Charlie', 22, 'Chicago'] ] # Loop through the data and add each row to the DataFrame for row_data in data_to_add: df.loc[len(df)] = row_data # Display the resulting DataFrame print(df)

In this example:

  1. We import the pandas library and create an empty DataFrame df with the desired column names ('Name', 'Age', 'City').

  2. We define the data that we want to add as rows in the data_to_add list of lists. Each inner list represents a row of data.

  3. We use a for loop to iterate through the data_to_add list and add each row of data to the DataFrame using the df.loc[len(df)] = row_data statement. Here, df.loc[len(df)] appends a new row to the DataFrame, and row_data contains the data for that row.

  4. Finally, we display the resulting DataFrame, which should contain the data added row by row.

You can customize this approach to fill a DataFrame with the specific data you have and the desired columns. Just make sure that the data you want to add as rows matches the structure and order of the columns in your DataFrame.

Examples

  1. Filling DataFrame Rows with a Loop

    • This query demonstrates how to fill a DataFrame row-by-row using a loop.
    • import pandas as pd # Create an empty DataFrame with specific columns df = pd.DataFrame(columns=['Name', 'Age', 'Country']) # Data to fill data = [ ['Alice', 25, 'USA'], ['Bob', 30, 'UK'], ['Charlie', 35, 'Canada'] ] # Fill row by row using a loop for row in data: df.loc[len(df)] = row print(df) # Output: # Name Age Country # 0 Alice 25 USA # 1 Bob 30 UK # 2 Charlie 35 Canada

  2. Filling DataFrame Rows with append

    • This query explores filling a DataFrame row-by-row using the append method.
    • import pandas as pd # Create an empty DataFrame df = pd.DataFrame(columns=['Name', 'Age', 'City']) # Data to append data = [ {'Name': 'Alice', 'Age': 25, 'City': 'New York'}, {'Name': 'Bob', 'Age': 30, 'City': 'London'}, {'Name': 'Charlie', 'Age': 35, 'City': 'Toronto'} ] # Append each row for d in data: df = df.append(d, ignore_index=True) print(df) # Output: # Name Age City # 0 Alice 25 New York # 1 Bob 30 London # 2 Charlie 35 Toronto

  3. Filling DataFrame Rows with concat

    • This query discusses filling a DataFrame row-by-row using concat and a list of dictionaries.
    • import pandas as pd # Data to concat data = [ {'Name': 'Alice', 'Age': 25, 'Score': 95}, {'Name': 'Bob', 'Age': 30, 'Score': 89}, {'Name': 'Charlie', 'Age': 35, 'Score': 92} ] # Concatenate into a DataFrame df = pd.concat([pd.DataFrame([d]) for d in data], ignore_index=True) print(df) # Output: # Name Age Score # 0 Alice 25 95 # 1 Bob 30 89 # 2 Charlie 35 92

  4. Filling DataFrame Rows with iloc

    • This query explores filling specific rows in a DataFrame using iloc.
    • import pandas as pd # Create a DataFrame with default values df = pd.DataFrame({'Value': [None] * 3, 'Status': [''] * 3}) # Data to fill data = [ {'Value': 10, 'Status': 'Completed'}, {'Value': 20, 'Status': 'Pending'}, {'Value': 30, 'Status': 'Completed'} ] # Fill DataFrame using iloc for idx, row in enumerate(data): df.iloc[idx] = row print(df) # Output: # Value Status # 0 10 Completed # 1 20 Pending # 2 30 Completed

  5. Filling DataFrame Rows from Another DataFrame

    • This query discusses filling a DataFrame row-by-row with data from another DataFrame.
    • import pandas as pd # Source DataFrame source_df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'Country': ['USA', 'UK', 'Canada'] }) # Target DataFrame with different structure target_df = pd.DataFrame(columns=['Name', 'Country', 'Age']) # Fill row-by-row from the source DataFrame for idx in range(len(source_df)): target_df.loc[len(target_df)] = source_df.iloc[idx] print(target_df) # Output: # Name Country Age # 0 Alice USA 25 # 1 Bob UK 30 # 2 Charlie Canada 35

  6. Filling DataFrame Rows with a List of Tuples

    • This query explores filling a DataFrame row-by-row with a list of tuples.
    • import pandas as pd # Data to fill (list of tuples) data = [ ('Alice', 25, 'Engineer'), ('Bob', 30, 'Doctor'), ('Charlie', 35, 'Teacher') ] # Create an empty DataFrame df = pd.DataFrame(columns=['Name', 'Age', 'Occupation']) # Fill row-by-row with tuples for t in data: df.loc[len(df)] = t print(df) # Output: # Name Age Occupation # 0 Alice 25 Engineer # 1 Bob 30 Doctor # 2 Charlie 35 Teacher

  7. Filling DataFrame Rows with Default Values

    • This query discusses filling DataFrame rows with default values.
    • import pandas as pd # Create a DataFrame with default values df = pd.DataFrame(columns=['Product', 'Price', 'Stock']) # Fill with default values for i in range(3): df.loc[i] = ['Product_' + str(i), 10.0, 100] print(df) # Output: # Product Price Stock # 0 Product_0 10.0 100 # 1 Product_1 10.0 100 # 2 Product_2 10.0 100

  8. Filling DataFrame Rows with Index and Range

    • This query explores how to fill a DataFrame row-by-row using index and range.
    • import pandas as pd # Create a DataFrame with index df = pd.DataFrame(index=range(3), columns=['ID', 'Status']) # Fill with unique IDs and status for idx in df.index: df.loc[idx] = [f'ID_{idx}', 'Active'] print(df) # Output: # ID Status # 0 ID_0 Active # 1 ID_1 Active # 2 ID_2 Active

  9. Filling DataFrame Rows with Random Values

    • This query discusses filling DataFrame rows with random values.
    • import pandas as pd import numpy as np # Create a DataFrame with random data df = pd.DataFrame(index=range(3), columns=['Random_1', 'Random_2']) # Fill with random values for idx in df.index: df.loc[idx] = [np.random.randint(1, 10), np.random.rand()] print(df) # Output (example): # Random_1 Random_2 # 0 3 0.7931 # 1 6 0.6781 # 2 1 0.2345

  10. Filling DataFrame Rows with Values Based on Conditions

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