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This is an analysis of Netflix in which the problem statements have been solved and which given in source code. This source code helps to clear several concepts of python pandas

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Analysis--2

The commands that we used in this project :

  • head() - It shows the first N rows in the data (by default, N=5).
  • tail () - It shows the last N rows in the data (by default, N=5).
  • shape - It shows the total no. of rows and no. of columns of the dataframe.
  • size - To show No. of total values(elements) in the dataset.
  • columns - To show each Column Name.
  • dtypes - To show the data-type of each column.
  • info() - To show indexes, columns, data-types of each column, memory at once.
  • value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.
  • unique() - It shows the all unique values of the series.
  • nunique() - It shows the total no. of unique values in the series.
  • duplicated( ) - To check row wise and detect the Duplicate rows.
  • isnull( ) - To show where Null value is present.
  • dropna( ) - It drops the rows that contains all missing values.
  • isin( ) - To show all records including particular elements.
  • str.contains( ) - To get all records that contains a given string.
  • str.split( ) - It splits a column's string into different columns.
  • to_datetime( ) - Converts the data-type of Date-Time Column into datetime[ns] datatype.
  • dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column.
  • groupby( ) - Groupby is used to split the data into groups based on some criteria.
  • sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph.
  • max( ), min( ) - It shows the maximum/minimum value of the series.
  • mean( ) - It shows the mean value of the series.

You will learn these things also: Creating New Columns & Dataframe Filtering (Single Column & Multiple Columns) Filtering with And and OR Seaborn Library - Bar Graphs

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This is an analysis of Netflix in which the problem statements have been solved and which given in source code. This source code helps to clear several concepts of python pandas

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