cleanassist 1.3.4

clean_assist is a simple library designed to help data scientists observe a descriptive summary of their DataFrame

Clean Assist

Clean Assist is a simple library designed to help data scientists observe a summary of any DataFrame they would like to clean. This library also displays charts to view the normal approximation of your variables.

Clean Assist is composed of 2 functions:

1. clean_assist.table(df, n_rows, n_round)
   
   Displays relevant features to help you on data cleaning and analysis.
   

   Parameters
   df            : DataFrame you would like to analyze
   n_rows    : Number of variables to display
   n_round   : Number of decimals to round calculations
   
   

2. clean_assist.normality(df, list_var, print_img, size_x, size_y, font_size)
   
   Displays histograms to compare the your variables to a normal distribution.
   

   Parameters
   df              : DataFrame you would like to analyze
   list_var    : Name of columns to analyze in a list format
   print_img    : input 'y' to print image or 'n' to not print
   size_x         : width of the image output
   size_y         : height of the image output
   font_size    : font size of the titles and headers
   

To import the library: copy paste the green colored code to your python code:

- Note: Delete the plus(+) signs after pasting code

+ import requests
+ url = 'https://raw.githubusercontent.com/juanduranc/Clean-Assist/master/library'
+ exec(requests.get(url).text)
+ help(clean_assist)

Example of library usage and interpretation:

1. The following table is a sample of an output form the function: clean_assist.table(df, n_rows, n_round)

VARIABLES	NULLS	COUNT	TYPES	MEAN	MEDIAN	UNIQUES	SAMPLE_________________________________	Outliers	pval(Norm)
AVG_CLICKS_PER_VISIT	0	1946	int64	13.5	13.0	15	[11, 13, 12, 13, 13, 17, 10, 13, 12, 12]	[6,0]	0.03
MEDIAN_MEAL_RATING	47	1899	int64	2.8	3.0	5	[3, 3, 3, 3, 3, 2, 4, 3, 3, 3]	[0,13]	3e-06
REVENUE	0	1946	float64	2107.3	1740.0	859	[1880, 1495, 2572.5, 1647, 1923, 1250]	[0,82]	1e-21
TOTAL_PHOTOS_VIEWED	0	1946	int64	106.4	0.0	371	[0, 90, 0, 0, 253, 0, 705, 0, 0, 0]	[0,120]	5e-90
CROSS_SELL_SUCCESS	0	1946	int64	0.7	1.0	2	[1, 1, 1, 0, 1, 1, 0, 1, 1, 1]		1e-159

Examples of findings:

• AVG_CLICKS_PER_VISIT has a similar mean and mean, it aproximates a normal distribution and has 6 lower outliers.
• MEDIAN_MEAL_RATING has 47 nulls which need imputation.
• Revenue is the only float variables, the rest are integer.
• TOTAL_PHOTOS_VIEWED has a median of 0 and 120 upper outliers. This means most people dont look view photos.
• CROSS_SELL_SUCCESS has 2 unique values. From the column named sample you can see only ones and zeros. This is a binary or boolean column.

2. Next, a sample output from the function: clean_assist.normality(df, list_var, print_img, size_x, size_y, font_size)


Histograms' interpretation:

• MEDIAN_MEAL_RATING has interger values and it mimisc a normal distribution.
• AVG_CLICKS_PER_VISIT is the colsest variable to a normal distribution with a p value of 0.03.
• REVENUE is right skewed with 82 upper outliers.
• TOTAL_PHOTOS_VIEWED has too many zero values. It is also right skewed and far from being a normal distribution.