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Inner Merge

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We frequently distribute relevant data across several tables in order to store it effectively.
For this tutorial, we will be using some datasets from an e-commerce business and we have three datasets namely Sales and Targets.

Let's check our datasets to get familiar with them.

import pandas as pd

sales = pd.read_csv('sales.csv')
targets = pd.read_csv('targets.csv')
print(sales.head())
print(targets.head())

In Pandas, we can merge two tables using merge() function and the merge function will look for the common column.
```
sales_vs_targets = pd.merge(sales, targets)
print(sales_vs_targets)
```
We can also merge them in this way too:
```
new_df = sales.merge(targets)
```
Which is the same as used in the above code demonstration.
In our table, we can also retrieve specific rows based on the condition we gave. For example, we are going to get only those rows from our new_df in which revenue > target
```
n = new_df[new_df.revenue >= new_df.target]
print(n)
```

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