Let's start with left and right joins. What does left join mean? Left join returns all the records from the left table, and matching records from the right table. If not all the records were matched, than remaining fields would be filled with NAs.

## Implementation in Python

All types of joins are implemented in `pandas` with the `df_left.merge(df_right, on = 'column', how = 'method')` function. There `df_left` is the left table, `df_right` is the right one, `on` - key field (column) that will be used for comparison, `how` - type of join.

For instance, let's perform a left join of two dataframes. Column with unique values in each dataframe is `id`. To perform a left join, set the `how = 'left'` parameter.

# Importing library
import pandas as pd

# Loading data
data1 = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/section5/data1.csv')
data2 = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/section5/data2.csv')

# Perform a left join
df_res = data1.merge(data2, on = 'id', how = 'left')
print(df_res)

As you can see, all the records from the `data1` table were left, and only matching record from the `data2` table were added.

This course covers intermediate topics on pandas, a must-have tool for each data analyst. During the course, you will learn how to prepare data for further interactions and how to group it using different techniques. You will learn the easiest data visualization and be acquainted with data joining.

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

Left Join

Implementation in Python