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Introduction

descriptive analytics

Welcome to the Analytics Snippet Collection, a curated selection of code examples and practical tips for working with analytics data.

Create Sample Data

By calling, describe.sample_data we can generate head and tail from dataframe.

describe.sample_data(df[["survived", "pclass", "sex", "age"]],n=10)

This function requires the following parameters:

  • main_data (dataframe): Data Input
  • n (int): total row

The result

  survived pclass sex age   survived pclass sex age  
0 0 3 male 22 < head tail > 0 3 male 33
1 1 1 female 38 < head tail > 0 3 female 22
2 1 3 female 26 < head tail > 0 2 male 28
3 1 1 female 35 < head tail > 0 3 male 25
4 0 3 male 35 < head tail > 0 3 female 39
5 0 3 male nan < head tail > 0 2 male 27
6 0 1 male 54 < head tail > 1 1 female 19
7 0 3 male 2 < head tail > 0 3 female nan
8 1 3 female 27 < head tail > 1 1 male 26
9 1 2 female 14 < head tail > 0 3 male 32

Interactive table

By calling, describe.df_to_sheet we can generate interactive table.

describe.df_to_sheet(df)

This function requires the following parameters:

  • main_data (dataframe): Data Input

The result

drawing

Numeric description

By calling, describe.describe_num we can generate more advance numeric description table.

describe.describe_num(df)

This function requires the following parameters:

  • main_data (dataframe): Data Input

The result

drawing

Generate higher and lower number from a column

By calling, describe.n_large_small we can generate n higher and lower number from specific column.

describe.n_large_small(main_data,col,n)

This function requires the following parameters:

  • main_data (dataframe): Data Input
  • col (string): column name
  • n (int): number or row to be shown

The result

  survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
803 1 3 male 0.42 0 1 8.5167 C Third child False nan Cherbourg yes False
755 1 2 male 0.67 1 1 14.5 S Second child False nan Southampton yes False
630 1 1 male 80 0 0 30 S First man True A Southampton yes True
851 0 3 male 74 0 0 7.775 S Third man True nan Southampton no True

Show value count for every unique in single column

By calling, describe.n_large_small we can generate value count for every unique in single column and calculate its percentage.

describe.value_counts(df,col)

This function requires the following parameters:

  • main_data (dataframe): Data Input
  • col (string): column name

The result

  sex n_samples percent
0 male 577 64.76
1 female 314 35.24

Show value count for every unique in each column

By calling, describe.n_large_small we can generate value count for every unique in multiple columns and calculate its percentage.

describe.value_counts_all(df,["sex",'survived'])

This function requires the following parameters:

  • main_data (dataframe): Data Input
  • col (list): column name

The result

drawing