Using iterrows() and DataFrame Affixing: A Step-by-Step Guide
Pandas is a powerful library used for data manipulation and analysis in Python. One of the most common operations performed on DataFrames is appending rows to an existing DataFrame.
However, this problem also includes another question - how can we insert a subset of columns from a single row of a DataFrame as a new row into another DataFrame with only 3 columns?
This can be solved by utilizing the iterrows() function and the DataFrame.append() method. This tutorial will walk you through each step to achieve this task.
Table of Contents
- Introduction to iterrows()
- Using DataFrame.append() for row appending
- Subsetting columns from a single row using loc
- Applying the solution to our example
Introduction to iterrows()
iterrows() is a method used in pandas DataFrames that allows you to iterate over each row index and value of the DataFrame.
# Example usage:
import pandas
df = pd.DataFrame({
'Name': ['Sanjay', 'Robin', 'Hugo'],
'Age': [34, 23, 65]
})
for index, row in df.iterrows():
print(f"Index: {index}")
print(f"Row Values: {row}")
Using DataFrame.append() for row appending
DataFrame.append() is used to append rows from another DataFrame.
# Example usage:
import pandas
df1 = pd.DataFrame({
'Name': ['Sanjay', 'Robin', 'Hugo'],
'Age': [34, 23, 65]
})
df2 = pd.DataFrame({
'Name': ['John', 'Jane']
})
print("DataFrame 1:")
print(df1)
print("\nDataFrame 2:")
print(df2)
# Append rows from df1 to df2
df2 = df2._append(df1, ignore_index=True)
print("\nUpdated DataFrame 2:")
print(df2)
However, using append() with the old style (_append) can lead to unexpected results and errors.
Subsetting columns from a single row using loc
loc[] is used to access rows and columns by label.
# Example usage:
import pandas
df = pd.DataFrame({
'Name': ['Sanjay', 'Robin', 'Hugo'],
'Age': [34, 23, 65],
'Phone': ['555-1212', '555-3322', '555-6655']
})
row_values = df.loc[0, ['Name', 'OrderNo', 'Phone']]
print(row_values)
Applying the solution to our example
We want to select a single row from df1 with columns ‘Name’, ‘OrderNo’, and ‘Phone’ and append it to df2.
# Example usage:
import pandas
import numpy
df1 = pd.DataFrame({
'Name': ['Sanjay', 'Robin', 'Hugo'],
'Email': ['<a>[email@sanjay.com](mailto:sanjay@sanjay.com)</a>','<a>[email@robin.com](mailto:robin@robin.com)</a>','<a>[email@hugo.com](mailto:hugo@hugo.com)</a>'],
'OrderNo': [23,234,66],
'Address': ['234 West Ave','45 Oak Street','Rt. 3443 FM290'],
'Phone': ['555-1212','555-3322','555-6655'],
'Age': [34,23,65]
})
df2 = pd.DataFrame(columns = ['Name', 'OrderNo', 'Phone'])
for index, row in df1.iterrows():
if index == 0:
# Select a single row with columns 'Name', 'OrderNo', and 'Phone'
new_row_values = row[['Name','OrderNo','Phone']]
print(f"New row values: {new_row_values}")
# Append the selected row to df2
df2 = df2._append(new_row_values, ignore_index=True)
print("\nUpdated DataFrame 2:")
print(df2)
However, using append() with the old style (_append) can lead to unexpected results and errors.
Instead of df2 = df2._append(new_row_values, ignore_index=True), use the new style:
# Example usage:
import pandas
df1 = pd.DataFrame({
'Name': ['Sanjay', 'Robin', 'Hugo'],
'Email': ['<a>[email@sanjay.com](mailto:sanjay@sanjay.com)</a>','<a>[email@robin.com](mailto:robin@robin.com)</a>','<a>[email@hugo.com](mailto:hugo@hugo.com)</a>'],
'OrderNo': [23,234,66],
'Address': ['234 West Ave','45 Oak Street','Rt. 3443 FM290'],
'Phone': ['555-1212','555-3322','555-6655'],
'Age': [34,23,65]
})
df2 = pd.DataFrame(columns = ['Name', 'OrderNo', 'Phone'])
for index, row in df1.iterrows():
if index == 0:
# Select a single row with columns 'Name', 'OrderNo', and 'Phone'
new_row_values = row[['Name','OrderNo','Phone']]
print(f"New row values: {new_row_values}")
# Append the selected row to df2 using the new style
df2 = pd.concat([df2, new_row_values], ignore_index=True)
print("\nUpdated DataFrame 2:")
print(df2)
Last modified on 2024-02-28