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code.py
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code.py
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# Meghana - CS20B1060
# Madhav - CS20B1047
# Web Application to perform Statistical Analysis, EDA & Data Visualisation
import streamlit as st
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
st.set_option('deprecation.showPyplotGlobalUse', False)
class DataFrame_Loader():
def __init__(self):
print("Loadind DataFrame")
def read_csv(self,data):
self.df = pd.read_csv(data)
return self.df
class EDA_Dataframe_Analysis():
def __init__(self):
print("General_EDA object created")
def show_dtypes(self,x):
return x.dtypes.astype(str)
def show_columns(self,x):
return x.columns
def show_hist(self,x):
return x.hist()
def Show_Missing(self,x):
y=x.isna().sum()
return y
def show_hist(self,x):
return x.hist()
def Tabulation(self,x):
table = pd.DataFrame(x.dtypes,columns=['dtypes'])
table1 =pd.DataFrame(x.columns,columns=['Names'])
table = table.reset_index()
table['No of Missing'] = x.isnull().sum().values
table['No of Uniques'] = x.nunique().values
table['Percent of Missing'] = ((x.isnull().sum().values)/ (x.shape[0])) *100
table['First Observation'] = x.loc[0].values
table['Second Observation'] = x.loc[1].values
table['Third Observation'] = x.loc[2].values
return table.astype(str)
def Numerical_variables(self,x):
Num_var = [var for var in x.columns if x[var].dtypes!="object"]
Num_var = x[Num_var]
return Num_var
def categorical_variables(self,x):
cat_var = [var for var in x.columns if x[var].dtypes=="object"]
cat_var = x[cat_var]
return cat_var
def impute(self,x):
df=x.dropna()
return df
class Attribute_Information():
def __init__(self):
print("Attribute Information object created")
def Column_information(self,data):
data_info = pd.DataFrame(
columns=['No of observation',
'No of Variables',
'No of Numerical Variables',
'No of Factor Variables',
'No of Categorical Variables',
'No of Logical Variables',
'No of Date Variables',
'No of zero variance variables'])
data_info.loc[0,'No of observation'] = data.shape[0]
data_info.loc[0,'No of Variables'] = data.shape[1]
data_info.loc[0,'No of Numerical Variables'] = data._get_numeric_data().shape[1]
data_info.loc[0,'No of Factor Variables'] = data.select_dtypes(include='category').shape[1]
data_info.loc[0,'No of Logical Variables'] = data.select_dtypes(include='bool').shape[1]
data_info.loc[0,'No of Categorical Variables'] = data.select_dtypes(include='object').shape[1]
data_info.loc[0,'No of Date Variables'] = data.select_dtypes(include='datetime64').shape[1]
data_info.loc[0,'No of zero variance variables'] = data.loc[:,data.apply(pd.Series.nunique)==1].shape[1]
data_info =data_info.transpose()
data_info.columns=['value']
data_info['value'] = data_info['value'].astype(int)
return data_info
def __get_missing_values(self,data):
#Getting sum of missing values for each feature
missing_values = data.isnull().sum()
#Feature missing values are sorted from few to many
missing_values.sort_values(ascending=False, inplace=True)
#Returning missing values
return missing_values
def __iqr(self,x):
return x.quantile(q=0.75) - x.quantile(q=0.25)
def __outlier_count(self,x):
upper_out = x.quantile(q=0.75) + 1.5 * self.__iqr(x)
lower_out = x.quantile(q=0.25) - 1.5 * self.__iqr(x)
return len(x[x > upper_out]) + len(x[x < lower_out])
def num_count_summary(self,df):
df_num = df._get_numeric_data()
data_info_num = pd.DataFrame()
i=0
for c in df_num.columns:
data_info_num.loc[c,'Negative values count']= df_num[df_num[c]<0].shape[0]
data_info_num.loc[c,'Positive values count']= df_num[df_num[c]>0].shape[0]
data_info_num.loc[c,'Zero count']= df_num[df_num[c]==0].shape[0]
data_info_num.loc[c,'Unique count']= len(df_num[c].unique())
data_info_num.loc[c,'Negative Infinity count']= df_num[df_num[c]== -np.inf].shape[0]
data_info_num.loc[c,'Positive Infinity count']= df_num[df_num[c]== np.inf].shape[0]
data_info_num.loc[c,'Missing Percentage']= df_num[df_num[c].isnull()].shape[0]/ df_num.shape[0]
data_info_num.loc[c,'Count of outliers']= self.__outlier_count(df_num[c])
i = i+1
return data_info_num
def statistical_summary(self,df):
df_num = df._get_numeric_data()
data_stat_num = pd.DataFrame()
try:
data_stat_num = pd.concat([df_num.describe().transpose(),
pd.DataFrame(df_num.quantile(q=0.10)),
pd.DataFrame(df_num.quantile(q=0.90)),
pd.DataFrame(df_num.quantile(q=0.95))],axis=1)
data_stat_num.columns = ['count','mean','std','min','25%','50%','75%','max','10%','90%','95%']
except:
pass
return data_stat_num
def main():
st.title("Summary Statistics, EDA & Data Visualisation")
# st.info("Upload your csv file here ;)")
st.subheader("Exploratory Data Analysis")
data = st.file_uploader("Upload your dataset here", type=["csv"])
if data is not None :
df = load.read_csv(data)
st.success("CSV File Loaded successfully")
listy1 = ['Select','Data Preview','Data Types','Columns','Number of missing values','Column Information','Show Selected Columns']
listy2=['Select','Aggregation Tabulation','Number Count Summary','Statistical Summary','Numerical Variables','Categorical Variables','DropNA']
option1 = st.sidebar.selectbox('Understanding Data',listy1)
option2 = st.sidebar.selectbox('Analysis of data',listy2)
if option1==listy1[1]:
st.subheader("Preview of your data : ")
st.dataframe(df.head())
if option1==listy1[2]:
st.subheader("Data Types : ")
st.write(dataframe.show_dtypes(df))
if option1==listy1[3]:
st.subheader("Columns in the dataset : ")
st.write(dataframe.show_columns(df))
if option1==listy1[4]:
st.subheader("Number of missing values : ")
st.write(dataframe.Show_Missing(df))
if option1==listy1[5]:
st.subheader("Column Information : ")
st.write(info.Column_information(df))
if option1==listy1[6]:
selected_columns = st.multiselect("Select Columns :",dataframe.show_columns(df))
st.subheader("Selected columns : ")
new_df = df[selected_columns]
st.dataframe(new_df)
if option2==listy2[1]:
st.subheader("Aggregation Table : ")
st.write(dataframe.Tabulation(df))
if option2==listy2[2]:
st.subheader("Number Count Summary : ")
st.write(info.num_count_summary(df))
if option2==listy2[3]:
st.subheader("Statistical Analysis : ")
st.write(info.statistical_summary(df))
if option2==listy2[4]:
st.subheader("Numerical Variables : ")
num_df = dataframe.Numerical_variables(df)
numer_df=pd.DataFrame(num_df)
st.dataframe(numer_df)
if option2==listy2[5]:
st.subheader("Categorical Variables : ")
new_df = dataframe.categorical_variables(df)
catego_df=pd.DataFrame(new_df)
st.dataframe(catego_df)
if option2==listy2[6]:
st.subheader("Drop NA : ")
num_df = dataframe.Numerical_variables(df)
imp_df = dataframe.impute(num_df)
st.dataframe(imp_df)
st.download_button(
label="Download data as CSV",
data=imp_df.to_csv(),
file_name='removed_Na.csv',
mime='text/csv',
)
listy3 = ['Select','Histogram','Bar graph','Box plot','Scatter plot','Dist plot','Frequency Distribution']
option3 = st.sidebar.selectbox('Univariate Analysis',listy3)
listy4 = ['Select','Bivariate Scattering','Heatmap','Multivariate']
option4 = st.sidebar.selectbox('Multivariate Analysis',listy4)
if option3==listy3[1]:
all_columns_names = dataframe.show_columns(df)
selected_columns_names = st.selectbox("Select Column for Histogram :",all_columns_names)
st.write(dataframe.show_hist(df[selected_columns_names]))
st.pyplot()
if option3==listy3[5]:
all_columns_names = dataframe.show_columns(df)
selected_columns_names = st.selectbox("Select Columns Distplot :",all_columns_names)
st.write(dataframe.Show_DisPlot(df[selected_columns_names]))
st.pyplot()
if __name__ == '__main__':
load = DataFrame_Loader()
dataframe = EDA_Dataframe_Analysis()
info = Attribute_Information()
main()