A data cleaning package
Project description
dstrial Module Documentation
Overview
This documentation explains the functions available in the Datacleaning
module, which is designed to assist in data cleaning and analytics tasks
Creating the an instance
Through this process, we are calling our class which will help us access the various functions to be used.
from dstrial import Datacleaning
data_cleaner = Datacleaning()
So for all the remaining part of our code, we shall be using the data_cleaner.
Table of Contents
- columns
- summary
- read_data
- head
- missing_values
- col_missing_value
- remove_empty_columns
- data_types
- cat_cols
- cont_cols
- distributions
- data_types
- col_dist
- cat_dist
- col_cat_dist
- remove_missingvalues
- drop
- outliers
- outliers_single
- remove_outliers
- corr_matrix
- cont_corr
- cont_to_cont
- cat_to_cat
- countplot
- contingency_table
- Chi_square
- combined_boplot
- singleAnova
- cont_to_cat
- getdata
- data_cleaning
columns
Function Name: columns
This function returns the columns of the loaded dataset.
Parameters
None
Return Value
- Returns a list of column names in the dataset.
summary
Function Name: summary
This function is used to generate summary statistics of the data. It provides valuable information about the distribution, central tendency, and spread of the data. It calculates statistics for each numeric column in the data.
The statistics provided by the summary function include:
- Count: The number of non-null values in the column.
- Mean: The arithmetic mean (average) of the values.
- Standard Deviation: A measure of the spread or dispersion of the values.
- Minimum: The minimum value in the column.
- 25th Percentile (Q1): The value below which 25% of the data falls.
- 50th Percentile (Median or Q2): The middle value of the data.
- 75th Percentile (Q3): The value below which 75% of the data falls.
- Maximum: The maximum value in the column.
Then to a categorical column
, The summary function generates statistics such as:
- Count: The number of non-null values in the column.
- Unique: The number of unique categories or levels in the column.
- Top: The most frequent category in the column.
- Freq: The frequency of the top category.
Parameters
None
Return Value
- Returns a dataframe containing the summary statistics of the data.
read_data
Function Name: read_data
This function is used to read data from a file. It supports reading data from a CSV file, Excel file, and a JSON file. The function automatically detects the file type and reads the data accordingly.
Parameters
file_path
: The path to the file to be read.
Return Value
- Returns a dataframe containing the data from the file.
head
Function Name: head
This function is used to display the first few rows of the data. It is useful to get a quick overview of the data.
Parameters
number
: The number of rows to display.
Return Value
- Returns a dataframe containing the first few rows of the data.
missing_values
Function Name: missing_values
This function is used to check for missing values in the data.
Parameters
None
Return Value
- Returns a dataframe containing the number of missing values in each column.
col_missing_value
Function Name: col_missing_value
This function is used to check for missing values in a specific column.
Parameters
col_name
: The name of the column to check for missing values.
Return Value
- Returns the number of missing values in the specified column.
remove_empty_columns
Function Name: remove_empty_columns
This function is used to remove columns that have no values. It is useful to remove columns that have no values as they do not provide any useful information.
Parameters
None
Return Value
- Returns a dataframe with the empty columns removed.
data_types
Function Name: data_types
This function is used to check the data types of the columns and the creates subsets of the data based on the data types. It creates a subset of the data containing only the categorical columns and another subset containing only the numeric columns.
Parameters
None
Return Value
None
cat_cols
Function Name: cat_cols
This function is used to get the categorical columns in the data.
Parameters
None
Return Value
- Returns a dataframe of the categorical columns in the data.
cont_cols
Function Name: cont_cols
This function is used to get the numeric columns in the data.
Parameters
None
Return Value
- Returns a dataframe of the numeric columns in the data.
distributions
Function Name: distributions
This function is used to plot the distribution of the numeric columns in the data. It plots a histogram for each numeric column in the data.
It is useful to get an idea of the distribution of the data. It can be used to identify outliers and skewness in the data.
Parameters
None
Return Value
None
col_dist
Function Name: col_dist
This function is used to plot the distribution of a specific numeric column in the data.
Parameters
col
: The name of the column to plot the distribution for.
Return Value
None
cat_dist
Function Name: cat_dist
This function is used to plot the distribution of a all categorical columns in the data.
Parameters
None
Return Value
None
col_cat_dist
Function Name: col_cat_dist
This function is used to plot the distribution of a specific categorical column in the data.
Parameters
col
: The name of the column to plot the distribution for.
Return Value
None
remove_missingvalues
Function Name: remove_missingvalues
This function is used to remove deal with rows that have missing values (NA). The funcion first removes all the duplicates that are within the data and also automatically removes all the empty columns.
The missing values are then replaced with the mode of the column (the most occuring value
) for categorical columns.
For numeric columns, the missing values are replaced with either the mean or median of the column depending on the skewness of the data.
Parameters
None
Return Value
None
drop
Function Name: drop
This function is used to drop columns from the data.
Parameters
column
: This is a two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the column to drop. If it is a list of strings, it is a list of columns to drop.
Return Value
None
outliers
Function Name: outliers
This function is used to plot the outliers in the data. It plots a boxplot for each numeric column in the data.
It is useful to get an idea of the outliers in the data. It can be used to identify outliers in the data.
Parameters
None
Return Value
None
outliers_single
Function Name: outliers_single
This function is used to plot the outliers in a specific numeric column in the data.
Parameters
column
: The name of the numeric column to plot the outliers for.
Return Value
None
remove_outliers
Function Name: remove_outliers
This function is used to remove outliers from the data. It removes outliers from all the numeric columns in the data.
The concept of outliers is based on the interquartile range (IQR). The IQR is the difference between the 75th percentile (Q3) and the 25th percentile (Q1). The IQR is used to identify outliers by defining limits on the sample values that are a factor k of the IQR below the 25th percentile or above the 75th percentile. The common value for the factor k is the value 1.5. This is the default value used by the function.
Parameters
None
Return Value
None
corr_matrix
Function Name: corr_matrix
This function is used to plot the correlation matrix of the data. It plots a heatmap of the correlation matrix of the data.
It is useful to get an idea of the correlation between the numeric columns in the data. It can be used to identify highly correlated columns in the data.
Parameters
None
Return Value
cont_corr
Function Name: cont_corr
This function is used to plot a pairplot of the numeric columns in the data.
Parameters
None
Return Value
None
cont_to_cont
Function Name: cont_to_cont
The function is used to show significant relationship or difference between two numeric columns in the data. This is achieved through plotting a scatter plot of two numeric columns in the data. The function also goes on to indicate the correlation value between the two columns.
Parameters
-
col1
: This is a two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the first column to plot. If it is a list of strings, it is a list of columns to plot. -
col2
: This is a two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the second column to plot. If it is a list of strings, it is a list of columns to plot.
Return Value
None
cat_to_cat
Function Name: cat_to_cat
The function is used to show significant relationship or difference between two categorical columns in the data. The function hence displays a contingency table of the two categorical columns in the data. and also plots a comparative bar graph of the two columns.
Parameters
-
col1
: This is a two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the first column to plot. If it is a list of strings, it is a list of columns to plot. -
col2
: This is a two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the second column to plot. If it is a list of strings, it is a list of columns to plot.
Return Value
None
countplot
Function Name: countplot
The function is used to plot a countplot of a two categorical columns in the data. This is a way of showing the distribution of the two categorical columns in the data.
Parameters
col1
: This is a string. It is the name of the first column to plot.col2
: This is a string. It is the name of the second column to plot.
Return Value
None
contingency_table
Function Name: contingency_table
The function is used to show significant relationship or difference between two categorical columns in the data. The function hence displays a contingency table of the two categorical columns in the data.
Parameters
col1
: This is a string. It is the name of the first column to plot.col2
: This is a string. It is the name of the second column to plot.
Return Value
None
Chi_square
Function Name: Chi_square
The function tests for a statistically significant relationship between nominal and ordinal variables. In other words, it tells us whether two variables are independent of one another.
Parameters
col1
: This is a string. It is the name of the first column categorical column.col2
: This is a string. It is the name of the second column categorical column.
Return Value
- A string indicating whether the two columns are independent or not.
combined_boplot
Function Name: combined_boplot
The function is used to plot a set of side by side box plots, one for each of the categories.
Parameters
col1
: This is a string. It is the name of the first column continuous column.col2
: This is a string. It is the name of the second column categorical column.
Return Value
None
singleAnova
Function Name: singleAnova
The function is used to test for a statistically significant difference between the means of two or more groups.
Parameters
col1
: This is a string. It is the name of the first column continuous column.col2
: This is a string. It is the name of the second column categorical column.
Return Value
- A string indicating whether the two columns are independent or not.
cont_to_cat
Function Name: cont_to_cat
The function is used to show significant relationship or difference between a continuous and a categorical column in the data.
The function hence displays a side by side boxplot of the continuous column and a categorical column in the data.
Parameters
-
col1
: This is two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the first column continuous column. If it is a list of strings, it is a list of columns to plot.On the other hand, it can be a a string or a list of strings of categorical columns. -
col2
: This is two way parameter. It can either be a string or a list of strings. If it is a string, it is the name of the second column categorical column. If it is a list of strings, it is a list of columns to plot.On the other hand, it can be a a string or a list of strings of continuous columns.
Return Value
- A string indicating whether the two columns are independent or not.
getdata
Function Name: getdata
The function returns the data that has been cleaned and preprocessed.
Parameters
None
Return Value
- Returns a dataframe containing the cleaned data.
data_cleaning
Function Name: data_cleaning
The function is used to clean the data. It performs the following operations:
- Removes empty columns.
- Removes duplicate rows.
- Deals with missing values appropriately.
- Removes outliers.
Parameters
None
Return Value
- A dataframe containing the cleaned data.
FUNCTIONS
Usage Example
from dstrial1 import Datacleaning
data_cleaner = Datacleaning()
columns_list = data_cleaner.columns()
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