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import pandas as pd
import numpy as np
from sklearn.impute import KNNImputer
import streamlit as st
# def remove_col(df ,i):
# df.drop([i], axis = 1,inplace = True)
# return df
# def column_delete(df, column_name):
# print("deleting the column: ", column_name)
# # new_df = (df.drop['column_name'], axis=1)
# del df[column_name]
# df.head()
# return df
# def row_delete(df, row_number):
# print("deleting the row number: ", row_number)
# df.drop(df.index[row_number])
# df.head()
# return df
# def mean_fill(df,column_name):
# mean_value=df[column_name].mean()
# filled = df[column_name].fillna(value=mean_value, inplace=True)
# return filled
# def median_fill(df,column_name):
# median_value=df[column_name].median()
# filled = df[column_name].fillna(value=median_value, inplace=True)
# return filled
# def random_fill(df):
# for i in df.columns:
# df[i+"_imputed"] = df[i]
# df[i+"_imputed"][df[i+"_imputed"].isnull()] = df[i].dropna().sample(df[i].isnull().sum()).values
# def EndDistribution(df, column_name):
# mean = df[column_name].mean()
# std = df[column_name].std()
# #calculating extreme standard deviation
# extreme = (mean + (3*std))
# df[column_name+'_median'] = df[column_name].fillna(df[column_name].median())
# df[column_name+'_end_distribution'] = df[column_name].fillna(extreme)
# return df
# #knn imputer
# def impute_knn(df):
# '''
# function for knn imputation in missing values in the data
# df - dataset provided by the users
# '''
# from sklearn.impute import KNNImputer
# imputer =KNNImputer(n_neighbors=5)
# #finding only numeric columns
# cols_num = df.select_dtypes(include=np.number).columns
# for feature in df.columns:
# #for numeric type
# if feature in cols_num:
# df[feature] = pd.DataFrame(imputer.fit_transform(np.array(df[feature]).reshape(-1, 1)))
# else:
# #for categorical type
# df[feature] = df[feature].fillna(df[feature].mode().iloc[0])
# return df
# #Z score capping
# def zScore(df):
# cols_num = df.select_dtypes(include=np.number).columns
# for i in cols_num:
# max_threshold = df[i].mean() + 3*df[i].std()
# min_threshold = df[i].mean() - 3*df[i].std()
# # df = df[(df['cgpa'] > 8.80) | (df['cgpa'] < 5.11)]
# df[i] = np.where(
# df[i]>max_threshold,
# max_threshold,
# np.where(
# df[i]<min_threshold,
# min_threshold,
# df[i]
# )
# )
# return df
# # zscore trimming
# def zScore_trim(df):
# cols_num = df.select_dtypes(include=np.number).columns
# for i in cols_num:
# max_threshold = df[i].mean() + 3*df[i].std()
# min_threshold = df[i].mean() - 3*df[i].std()
# df = df[(df[i] < max_threshold) | (df[i] > min_threshold)]
# return df
# # Ourlier using Percentile
# # trimming
# def percentile_trimming(df):
# cols_num = df.select_dtypes(include=np.number).columns
# for i in cols_num:
# percentile25 = df[i].quantile(0.25)
# percentile75 = df[i].quantile(0.75)
# iqr = percentile75 - percentile25
# max_threshold = percentile75 + 3*iqr
# min_threshold = percentile25 - 3*iqr
# df = df[(df[i] < max_threshold) | (df[i] > min_threshold)]
# return df
# #capping
# def percentile_capping(df):
# cols_num = df.select_dtypes(include=np.number).columns
# for i in cols_num:
# percentile25 = df[i].quantile(0.25)
# percentile75 = df[i].quantile(0.75)
# iqr = percentile75 - percentile25
# max_threshold = percentile75 + 3*iqr
# min_threshold = percentile25 - 3*iqr
# df[i] = np.where(
# df[i]>max_threshold,
# max_threshold,
# np.where(
# df[i]<min_threshold,
# min_threshold,
# df[i]
# )
# )
# return df
# # Function to find date column in dataframe and convert it to datetime format
# def convert_date(df):
# '''
# function parameter : dataframe
# parameter datatype : pandas.core.frame.DataFrame
# function returns : dataframe
# return datatype : pandas.core.frame.DataFrame
# function definition : takes dataframe as input and finds the date columns in the dataframe.
# if found, converts the column to datetime format.
# '''
# df = df.apply(lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col, axis=0)
# return df
# # Function to find price column in dataframe
# def price_column(df):
# '''
# function parameter : dataframe
# parameter datatype : pandas.core.frame.DataFrame
# function returns : dataframe
# return datatype : pandas.core.frame.DataFrame
# function definition : takes dataframe as input and finds the price related columns in the dataframe.
# if found, renames the column to price_1.
# '''
# numeric_cols = [col for col in df.columns if df[col].dtype in ['int64', 'float64']]
# price_cols = [col for col in numeric_cols if col.lower().find('price') != -1 or col.lower().find('cost') != -1 or
# col.lower().find('total') != -1 or col.lower().find('amount') != -1 or col.lower().find('revenue') != -1 or
# col.lower().find('profit') != -1 or col.lower().find('margin') != -1 or col.lower().find('sales') != -1]
# if len(price_cols) > 1:
# for i in range(len(price_cols)):
# df.rename(columns={price_cols[i]: 'price_'+str(i+1)}, inplace=True)
# elif len(price_cols) == 1:
# df.rename(columns={price_cols[0]: 'price'}, inplace=True)
# return df
# def data_cleaning(df):
# import pandas as pd
# import numpy as np
# from sklearn.impute import KNNImputer
# pd.set_option('display.max_rows', 100)
# for i in df.columns:
# if ((df[i].isna().sum())/df.shape[0]) > 0.95:
# df = remove_col(df,i)
# else:
# df = df.copy()
# df = impute_knn(df)
# return df
# class missing_df:
# def __init__(self, df):
# self.df = df
# print(self.df)
#functions for handling missing values
class missing_df:
def __init__ (self,dataset):
self.dataset = dataset
def handle_missing_value():
df = pd.read_csv("temp_data/test.csv")
missing_count = df.isnull().sum().sum()
if missing_count != 0:
print(f"Found total of {missing_count} missing values.")
#remove column having name starts with Unnamed
df =df.loc[:,~df.columns.str.startswith('Unnamed')]
#drop columns having more than 90% missing values
for i in df.columns.to_list():
if df[f"{i}"].isna().mean().round(4) > 0.9:
df = df.drop(i, axis=1)
#converting object datatype to integer if present
for j in df.columns.values.tolist(): # Iterate on columns of dataframe
try:
df[j] = df[j].astype('int') # Convert datatype from object to int, of columns having all integer values
except:
pass
# find date column in dataframe and convert it to datetime format
try:
df = df.apply(lambda col: pd.to_datetime(col, errors='ignore') if col.dtypes == object else col, axis=0)
except:
pass
#impute missing values
imputer = KNNImputer(n_neighbors=3)
#finding numerical columns from dataset
cols_num = df.select_dtypes(include=np.number).columns
for feature in df.columns:
#for numeric type
if feature in cols_num:
df[feature] = pd.DataFrame(imputer.fit_transform(np.array(df[feature]).reshape(-1, 1)))
else:
#for categorical type
df[feature] = df[feature].fillna(df[feature].mode().iloc[0])
# def add_binary_col(df):
# """
# Functions to add binary column which tells if the data was missing or not
# """
# for label, content in df.items():
# if pd.isnull(content).sum():
# df["ismissing_"+label] = pd.isnull(content)
# return df
st.write(df)
return df
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