Pandas – Append a List as a Row to DataFrame

Pandas support several ways to append a list as a row to DataFrame, In this article, I will explain how to append a Python list as a row where it adds a new row to the DataFrame with elements specified by a list.

You can use the append() method, loc[], and iloc[] properties to append/add a list of values as a row in Pandas, let’s see these with examples. Use loc vs iloc to understand the differences.

Key Points –

• Allows appending a list as a row at a specified index. Use df.loc[len(df)] = list to add the list at the end.
• Enables setting values at specific integer-based index positions. Use df.iloc[index] = list to insert a row at a particular position.
• The append() function can add a list as a row by converting it into a DataFrame or Series.
• With loc or iloc, you can place a new row at any desired position within the DataFrame.
• When using append(), specify ignore_index=True to reset the index after appending.
• When using append(), convert the list to a DataFrame and set column names to ensure consistency.

Quick Examples of Pandas Append List to DataFrame

Below are some quick examples of appending a list as a row to DataFrame.

# Quick examples of pandas append list to DataFrame

# Example 1: New list to append row to DataFrame
list = ["Hyperion", 27000, "60days", 2000]
df.loc[len(df)] = list

# Example 2: Added at second position
df.iloc[1] = list

# Example 3: Using append()
list = ["Bigdata", 27000, "40days", 2800]
df2 = df.append(pd.DataFrame([list], 
     columns=["Courses","Fee","Duration","Discount"]), 
     ignore_index=True)

# Example 4: A series object with the same index as DataFrame
df2 = df.append(pd.Series(list, index = ["Courses","Fee","Duration","Discount"]), 
           ignore_index=True)

Let’s create a DataFrame with a few rows and columns then, execute some examples and validate the results. Our DataFrame contains column names Courses, Fee, Duration and Discount.

# Create DataFrame
import pandas as pd
technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Duration':['30days','50days','35days', '40days','55days'],
    'Discount':[1000,2300,1000,1200,2500]
          }
df = pd.DataFrame(technologies)
print(" Create DataFrame:\n", df)

Yields below output.

Using loc[] to Append the New List to a DataFrame

By using df.loc[index]=list you can append a list as a row to the DataFrame at a specified Index, In order to add at the end get the index of the last record using the len(df) function. The below example adds the list ["Hyperion",27000,"60days",2000] to the end of the Pandas DataFrame.

# New list to append Row to DataFrame
list = ["Hyperion", 27000, "60days", 2000]
df.loc[len(df)] = list
print("After appending a list as a row:\n", df)

Yields below output.

Use df.iloc[1]=list to append the row to the second position of the DataFrame as the Index starts from zero.

# New list to append DataFrame
list = ["Oracle", 20000, "60days", 2000]
# using iloc[] method
df.iloc[1] = list
print("After appending a list as a row:\n", df)

Yields below output.

# Output:
# After appending a list as a row:
  Courses    Fee Duration  Discount
0   Spark  22000   30days      1000
1  Oracle  20000   60days      2000
2  Hadoop  23000   35days      1000
3  Python  24000   40days      1200
4  Pandas  26000   55days      2500

Using DataFrame.append() to Add or Append a List as a Row

Alternatively, you can use DataFrame.append() function to add a row to the DataFrame. This by default adds a row at the end. You can Get Column Names as a List From Pandas DataFrame and use it in this example.

Related: You can also append two DataFrames using the append() function.

# New list for append into DataFrame
list = ["Bigdata", 27000, "40days", 2800]

# Using append to add the list to DataFrame
df2 = df.append(pd.DataFrame([list], columns=["Courses","Fee","Duration","Discount"]), ignore_index=True)
print("After appending a list as a row:\n", df2)

Yields below output.

# Output:
# After appending a list as a row:
   Courses    Fee Duration  Discount
0    Spark  22000   30days      1000
1  PySpark  25000   50days      2300
2   Hadoop  23000   35days      1000
3   Python  24000   40days      1200
4   Pandas  26000   55days      2500
5  Bigdata  27000   40days      2800

Append Series as a Row to the DataFrame

Using append() you can also append series as a row to the DataFrame.

# New list for append into DataFrame
list = ["Bigdata", 27000, "40days", 2800]

# A series object with the same index as DataFrame
df2 = df.append(pd.Series(list, index = ["Courses","Fee","Duration","Discount"]), ignore_index=True)
print("After appending a list as a row:\n", df2)

Yields below output.

# Output:
# After appending a list as a row:
   Courses    Fee Duration  Discount
0    Spark  22000   30days      1000
1  PySpark  25000   50days      2300
2   Hadoop  23000   35days      1000
3   Python  24000   40days      1200
4   Pandas  26000   55days      2500
5  Bigdata  27000   40days      2800

FAQ on Append a List as a Row to Pandas DataFrame

Conclusion

In this article, you have learned how to append a list as a row to Pandas DataFrame using DataFrame.loc[], DataFrame.iloc[], and DataFrame.append() methods. Using these you can append a row at any position/index.

Happy Learning !!

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