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These rows will have the value NaN in the column Unemployment Rate of the concatenated DataFrame, as shown
below:
>>> # Concatenate DataFrames row-wise
>>> gdpDF = pd.concat([gdpDF1, gdpDF2], ignore_index=True)
>>> print(gdpDF)
Year Gross Domestic Product Inflation Rate Unemployment Rate
0 2018 21.3 2.1 NaN
1 2019 22.6 1.8 NaN
2 2020 20.9 2.5 NaN
3 2021 23.2 2.5 4.2
4 2022 24.6 2.0 4.4
By default, the method pd.concat() concatenates the two Dataframes rows wise (axis = 0). However, we can
also concatenate the DataFrames column wise by setting keyword argument axis = 1. For example, suppose, the
first Dataframe contains information regarding expenses (Rent, Utilities, Groceries) for first 4 months of the year and
second Dataframe contains information regarding monthly income and investments (Salary, Bonus, Investments) for
the same month as shown below:
>>> import pandas as pd
>>> # Create the first DataFrame for monthly expenses
>>> data1 = {'Month': ['January', 'February', 'March', 'April'],
'Rent': [1200, 1200, 1200, 1300],
'Utilities': [150, 170, 160, 165],
'Groceries': [300, 350, 320, 350]}
>>> df1 = pd.DataFrame(data1)
>>> # Create the second DataFrame for monthly income
>>> data2 = {'Month_df2': ['January', 'February', 'March', 'April'],
'Salary': [4000, 4000, 4000, 4000],
'Bonus': [500, 600, 550, 600],
'Investments': [1000, 900, 1100, 900]}
>>> df2 = pd.DataFrame(data2)
print("Monthly Expenses:\n", df1,"\n")
print("Monthly Income:\n", df2, '\n')
# Concatenate DataFrames column-wise
df = pd.concat([df1, df2], axis=1)
print(df.head())
output:
Monthly Expenses:
Month Rent Utilities Groceries
0 January 1200 150 300
1 February 1200 170 350
2 March 1200 160 320
3 April 1300 165 350
Monthly Income:
Month_df2 Salary Bonus Investments
0 January 4000 500 1000
1 February 4000 600 900
2 March 4000 550 1100
3 April 4000 600 900
Month Rent Utilities Groceries Month_df2 Salary Bonus Investments
0 January 1200 150 300 January 4000 500 1000
1 February 1200 170 350 February 4000 600 900
2 March 1200 160 320 March 4000 550 1100
3 April 1300 165 350 April 4000 600 900
58 Touchpad Informatics Practices-XII

