Let's move on to the first visualization steps. By now you already know how to clean, prepare, and aggregate data for further analysis. We'll start with histograms.

What is a histogram? Histogram is a graph that represents frequencies of numerical data (usually numerical intervals). To build histogram in `pandas`, apply the `.hist()` method to selected data. For instance, let's build a histogram for the `'totinch'` column.

> Note that you don't need to use the `print()` function to output the plot.

# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv')

# Histogram for the totinch column values
df.totinch.hist()

 As parameters, you can set `color` (color for rectangles, like `'r'`, `'g'`, `'b'`, etc.) or `bins` (number of intervals to divide data). Let's make rectangles red and set the number of intervals to 50.

# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data4.csv')

# Histogram for the totinch column values
df.totinch.hist(color = 'r', bins = 50)

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.

Data Manipulation using pandas

Histograms

Data Manipulation using pandas

Histograms