There are multiple ways to add or insert a row to pandas DataFrame, in this article, I will explain how to add a row to DataFrame with several examples by using append(), pandas.concat(), and loc[]
The examples include how to insert or add a python list, dict (dictionary) as a row to pandas DataFrame, which ideally adds a new record to the DataFrame with elements specified by a list and dict.
1. Quick Examples of Add Row to DataFrame
If you are in a hurry, below are some quick examples of how to insert/add a row to DataFrame.
# Below are quick example
# add Row to DataFrame
list_row = ["Hyperion", 27000, "60days", 2000]
df.loc[len(df)] = list_row
# Insert Dict to the dataframe using DataFrame.append()
new_row = {'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}
df2 = df.append(new_row, ignore_index=True)
# Add new row to specifig index name
df2 = df.append(pd.DataFrame([new_row],index=['7'],columns=df.columns))
# Append row to the DataFrame
df2 = df.append(pd.Series(new_row, index=df.columns, name='7'))
# Using pandas.concat() to add a row
new_row = pd.DataFrame({'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}, index=[0])
df2 = pd.concat([new_row,df.loc[:]]).reset_index(drop=True)
# Add specific row/index name using DataFrame.loc[]
df.loc['7', :] = ['Hive',25000,'45days',2000]
# Add row in DataFrame using DataFrame.loc[]
df.loc['7'] = ['Hive',25000,'45days',2000]
Let’s create a pandas DataFrame from Dict with a few rows and columns and execute some examples to learn how to insert rows. Our Pandas DataFrame contains column names Courses, Fee, Duration, and Discount.
import pandas as pd
technologies = ({
'Courses':["Spark","Hadoop","pandas","Java","Pyspark"],
'Fee' :[20000,25000,30000,22000,26000],
'Duration':['30days','40days','35days','60days','50days'],
'Discount':[1000,2500,1500,1200,3000]
})
df = pd.DataFrame(technologies)
print(df)
Yields below output.
Courses Fee Duration Discount
0 Spark 20000 30days 1000
1 Hadoop 25000 40days 2500
2 pandas 30000 35days 1500
3 Java 22000 60days 1200
4 Pyspark 26000 50days 3000
2. Add Row to Pandas DataFrame
By using append() function you can add or insert a row to existing pandas DataFrame from the dict. This method is required to take ignore_index=True in order to add a dict as a row to DataFrame, not using this will get you an error. This method returns the new DataFrame with the newly added row.
# Insert row to the dataframe using DataFrame.append()
df = pd.DataFrame(technologies)
new_row = {'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}
df2 = df.append(new_row, ignore_index=True)
print(df2)
Yields below output.
Courses Fee Duration Discount
0 Spark 20000 30days 1000
1 Hadoop 25000 40days 2500
2 pandas 30000 35days 1500
3 Java 22000 60days 1200
4 Pyspark 26000 50days 3000
5 Hyperion 24000 55days 1800
Note that when you used ignore_index=True, it ignores the existing index on the DataFrame and set a new index.
3. Add or Insert List as Row to DataFrame
If you have a list and want to add/insert it to DataFrame use loc[]. For more similar examples, refer to how to append a list as a row to pandas DataFrame.
# New list to append Row to DataFrame
list = ["Hyperion", 27000, "60days", 2000]
df.loc[len(df)] = list
print(df)
Note that when you have a default number index, it automatically increments the index and adds the row at the end of the DataFrame.
4. Add Row to DataFrame with Index Name
Alternatively, you can also use DataFrame.append() function to add a new row to pandas DataFrame with a custom Index. Note that in this example, first we are creating a new DataFrame/Series and append this to another DataFrame.
# Add new row to specifig index name
df2 = df.append(pd.DataFrame([new_row],index=['7'],columns=df.columns))
print(df2)
# Add row to the DataFrame using append()
df2 = df.append(pd.Series(new_row, index=df.columns, name='7'))
print(df2)
Yields below output for both examples.
Courses Fee Duration Discount
0 Spark 20000 30days 1000
1 Hadoop 25000 40days 2500
2 pandas 30000 35days 1500
3 Java 22000 60days 1200
4 Pyspark 26000 50days 3000
7 Hyperion 24000 55days 1800
5. Use concat() to Add Row at Top of DataFrame
Use pd.concat([new_row,df.loc[:]]).reset_index(drop=True) to add the row to the first position of the Pandas DataFrame as Index starts from zero. reset_index() will reset the Index on the DataFrame to adjust the indexes on other rows.
# Using pandas.concat() to add a row
new_row = pd.DataFrame({'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}, index=[0])
df2 = pd.concat([new_row,df.loc[:]]).reset_index(drop=True)
print (df2)
Yields below output.
Courses Fee Duration Discount
0 Hyperion 24000 55days 1800
1 Spark 20000 30days 1000
2 Hadoop 25000 40days 2500
3 pandas 30000 35days 1500
4 Java 22000 60days 1200
5 Pyspark 26000 50days 3000
6. Use DataFrame.loc[] to Add Specific Row/Index Name
By using df.loc[Index_label]=new_row you can add a list as a row to the DataFrame at a specified index, In add at the end get the index of the last record using DataFrame.loc['Index5',:] function.
# Add specific row/index name using DataFrame.loc[]
df.loc['7', :] = ['Hive',25000,'45days',2000]
print(df)
# Add row in DataFrame using DataFrame.loc[]
df.loc['7'] = ['Hive',25000,'45days',2000]
print(df)
Yields below output.
Courses Fee Duration Discount
0 Spark 20000.0 30days 1000.0
1 Hadoop 25000.0 40days 2500.0
2 pandas 30000.0 35days 1500.0
3 Java 22000.0 60days 1200.0
4 Pyspark 26000.0 50days 3000.0
7 Hive 25000.0 45days 2000.0
7. Complete Example of Add Row to DataFrame
import pandas as pd
technologies = ({
'Courses':["Spark","Hadoop","pandas","Java","Pyspark"],
'Fee' :[20000,25000,30000,22000,26000],
'Duration':['30days','40days','35days','60days','50days'],
'Discount':[1000,2500,1500,1200,3000]
})
df = pd.DataFrame(technologies)
print(df)
# Insert row to the dataframe using DataFrame.append()
df = pd.DataFrame(technologies)
new_row = {'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}
df2 = df.append(new_row, ignore_index=True)
print(df2)
# Add new row to specifig index name
new_row = {'Courses':'Oracle','Fee':25000,'Duration':'65days','Discount':2800}
df2 = df.append(pd.DataFrame([new_row],index=['Index'],columns=df.columns))
print(df2)
# Append row to the DataFrame
df2 = df.append(pd.Series(new_row, index=df.columns, name='Index'))
print(df2)
# Using pandas.concat() to add a row
new_row = pd.DataFrame({'Courses':'Hyperion', 'Fee':24000, 'Duration':'55days', 'Discount':1800}, index=[0])
df2 = pd.concat([new_row,df.loc[:]]).reset_index(drop=True)
print (df2)
# Add specific row/index name using DataFrame.loc[]
df.loc['Index5', :] = ['Hive',25000,'45days',2000]
print(df)
# Add row in DataFrame using DataFrame.loc[]
df.loc['Index5'] = ['Hive',25000,'45days',2000]
print(df)
Conclusion
In this article, you have learned how to add or insert a row to Pandas DataFrame using loc[], concat(), and append() methods. Using these you can add a row from list/dict at any position/index.
Happy Learning !!
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