Pandas – Append a List as a Row to DataFrame

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  • Post category:Pandas / Python
  • Post last modified:January 16, 2022

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 ita adds a new rows to the DataFrame with elements specified by a list.

You can use append() method and loc[], 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.

1. Quick Examples of pandas append list to DataFrame

Below are some quick examples that aappend list as row to DataFrame.


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

# Addes at second position
df.iloc[1] = list

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

# 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 and execute some examples and validate the results. Our DataFrame contains column names Courses, Fee, Duration and Discount.


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(df)

Yields below output.


   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

2. 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 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(df)

Yields below output.


    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  Hyperion  27000   60days      2000

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


# New list to append DataFrame
list = ["Oracle", 20000, "60days", 2000]
# using iloc[] method
df.iloc[1] = list
print(df)

Yields below output.


  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

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

Alternatively, you can also 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.


# 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(df2)

Yields below output.


   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

4. 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(df2)

Yields below output.


   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

Conclusion

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

Happy Learning !!

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