{'Column1': 'Value1', 'Column2': 'Value2'} to create your desired populated DataFrame.Method 1: Using DataFrame.loc[]
The DataFrame.loc[] method allows you to access a group of rows and columns by labels. When you have an empty DataFrame, you can use it to append a new row by specifying an index for the new row and setting the values for each column.
Here’s an example:
import pandas as pd # Create an empty DataFrame with column names df = pd.DataFrame(columns=['Column1', 'Column2']) # Append a row to DataFrame using DataFrame.loc df.loc[len(df)] = ['Value1', 'Value2'] print(df)
Output:
Column1 Column2 0 Value1 Value2
This code snippet starts by importing the pandas library and creating an empty DataFrame with specified column names. Using df.loc[len(df)], it appends a new row at the end of the DataFrame. The len(df) provides the index where the new row should be placed.
Method 2: Using DataFrame.append()
The append() function is a straightforward way of adding rows to a DataFrame. It takes a dictionary or another DataFrame and appends it to the original DataFrame, returning a new DataFrame object. This method is especially useful when appending multiple rows within a loop.
Here’s an example:
import pandas as pd
# Create an empty DataFrame with column names
df = pd.DataFrame(columns=['Column1', 'Column2'])
# Append a row to DataFrame using a dictionary
row = {'Column1': 'Value1', 'Column2': 'Value2'}
df = df.append(row, ignore_index=True)
print(df)Output:
Column1 Column2 0 Value1 Value2
This snippet also imports the pandas library and defines an empty DataFrame with column names. You can append a new row using the append() method with ignore_index=True, which disregards the index labels and instead adds a new numerical index.
Method 3: Using pandas.concat()
The pandas.concat() function is utilized for concatenating pandas objects along a particular axis. By using concat(), you can join a temporary DataFrame containing your new row with your existing empty DataFrame to append the row.
Here’s an example:
import pandas as pd # Create an empty DataFrame with column names df = pd.DataFrame(columns=['Column1', 'Column2']) # Create a new DataFrame with the row to append new_row = pd.DataFrame([['Value1', 'Value2']], columns=['Column1', 'Column2']) # Append the row using pandas.concat df = pd.concat([df, new_row], ignore_index=True) print(df)
Output:
Column1 Column2 0 Value1 Value2
After creating an empty DataFrame, this code creates a second DataFrame containing the row to be appended. Using pd.concat() with the parameter ignore_index=True, it appends the row to the empty DataFrame and resets the index properly.
Method 4: Using DataFrame.assign()
The assign() method encourages a functional approach to modifying DataFrames. When used correctly, it can be leveraged to append a row to an empty DataFrame although this is less conventional and a more indirect method.
Here’s an example:
import pandas as pd
# Create an empty DataFrame
df = pd.DataFrame()
# Unconventionally append a row using DataFrame.assign() and a temporary column
temporary_df = df.assign(temporary_column=0)
temporary_df = temporary_df.append({'temporary_column': 1}, ignore_index=True)
df = temporary_df.drop('temporary_column', axis=1)
df['Column1'], df['Column2'] = 'Value1', 'Value2'
print(df)Output:
Column1 Column2 0 Value1 Value2
This method starts by creating an empty DataFrame and then adds a new column with the assign() method. A new row is then appended using the previously mentioned append() method, followed by cleanup steps to establish the final DataFrame.
Bonus One-Liner Method 5: Using a Single Line of Code
For those looking for a quick, one-liner solution, you can append a row directly with a combination of DataFrame constructor and assignment.
Here’s an example:
import pandas as pd
# Create an empty DataFrame and append a new row in one line
df = pd.DataFrame([], columns=['Column1', 'Column2']).append({'Column1': 'Value1', 'Column2': 'Value2'}, ignore_index=True)
print(df)Output:
Column1 Column2 0 Value1 Value2
This one-liner effectively combines the creation of the empty DataFrame with the appending of a new row using the append() method and specified column names, all in a single statement.
Summary/Discussion
- Method 1: Using DataFrame.loc[]. Useful for adding rows based on index. Less optimal if column names are not predefined.
- Method 2: Using DataFrame.append(). Straightforward and easy to read. Although convenient, it can be less efficient with large data sets because it returns a new DataFrame.
- Method 3: Using pandas.concat(). Offers flexibility in concatenation operations. It may be more verbose compared to other methods.
- Method 4: Using DataFrame.assign(). Less conventional for appending rows; more complex and not as intuitive.
- Method 5: Bonus one-liner. Quick and efficient for adding a single row but may become less manageable with more complex operations.