Exploratory Data Analysis (EDA) for Udemy and Weather Analysis

This is a project which helps to works on real Data using Python. ##Projects

• Udemy Exploration
• Weather Exploration

UDEMY EXPLORATION

Udemy Courses Dataset is analyzed in a very Easy To Understand (ETU) language.

Udemy Dataset

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Commands

• import pandas as pd -- To import Pandas library.
• pd.read_csv - To import the CSV file in Jupyter notebook.
• head() - It shows the first N rows in the data (by default, N=5).
• unique( ) - It shows the all unique values of the column.
• value_counts - In a column, it shows all the unique values with their count. It can be applied to a single column only.
• df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1.
• df.sort_values(‘Col_name' , ascending=False ) - To sort the dataframe wrt any column values in descending order.
• df[ (df.Col1 = = ‘Element1’) & (df.Col2 == ‘Element2’) ] - Multilevel filtering - And Filter – Filtering the data with two & more items.
• str.contains('Value_to_match’) - To find the records that contains a particular string.
• dtypes - To show datatypes of each column.
• pd.to_datetime(df.Date_Time_Col) - To convert the data-type of Date-Time Column into datetime[ns] datatype.
• dt.year - Creating a new column with only year values.
• df.groupby(‘Col_1’)['Col_2'].max() - Using groupby with two different columns.

WEATHER EXPLORATION

Weather data is analysed in same method did in udemy exploration but using different Commands.

Weather Dataset

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Commands

• head() - It shows the first N rows in the data (by default, N=5).
• shape - It shows the total no. of rows and no. of columns of the dataframe
• index - This attribute provides the index of the dataframe
• columns - It shows the name of each column
• dtypes - It shows the data-type of each column
• unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.
• nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.
• count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe.
• value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.
• info() - Provides basic information about the dataframe.