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updated-file.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
import seaborn as sns
import plotly.express as px
import time
from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts
from htbuilder.units import percent, px
from htbuilder.funcs import rgba, rgb
st.set_page_config(
page_title="Summary Statistics, EDA & Data Visualisation",
page_icon="📊",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
'About': "# Our Data Science Project. A GUI to analyze and visualize your dataset."
}
)
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_scatterplt(self,a,x,y):
return px.scatter(x=x, y=y)
def show_bar(self,a,x,y):
return px.histogram(a, x=x, y=y)
def show_boxplt(self,x):
return sns.boxplot(data=x)
def show_violinplt(self,x):
return sns.violinplot(data=x)
def show_displot(self,x):
return sns.displot(data=x)
def show_heatmp(self,x):
return sns.heatmap(x,annot=True)
def Show_Missing(self,x):
y=x.isna().sum()
return y
def plotly(self,a,x,y):
fig = px.scatter(a, x=x, y=y)
fig.update_traces(marker=dict(size=10,
line=dict(width=2,
color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.show()
def plotly_histogram(self,a,x,y):
fig = px.histogram(a, x=x, y=y)
fig.update_traces(marker=dict(size=10,
line=dict(width=2,
color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.show()
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)
# my_bar = st.progress(0)
# for percent_complete in range(91):
# time.sleep(0.01)
# my_bar.progress(percent_complete + 10)
success_txt = st.success("CSV File Loaded successfully",icon="✅")
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 : ")
# Boolean to resize the dataframe, stored as a session state variable
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','Violin plot','Box plot','Dist plot']
option3 = st.sidebar.selectbox('Univariate Analysis',listy3)
num_df = dataframe.Numerical_variables(df)
numer_df=pd.DataFrame(num_df)
# histogram
if option3==listy3[1]:
all_columns_names = dataframe.show_columns(numer_df)
selected_columns_names = st.selectbox("Select Column for Histogram :",all_columns_names)
st.write(dataframe.show_hist(df[selected_columns_names]))
st.pyplot()
# violin plot
if option3==listy3[2]:
all_columns_names = dataframe.show_columns(numer_df)
selected_columns_names = st.selectbox("Select Column for Violin plot :",all_columns_names)
st.write(dataframe.show_violinplt(df[selected_columns_names]))
st.pyplot()
if option3==listy3[4]:
all_columns_names = dataframe.show_columns(numer_df)
selected_columns_names = st.selectbox("Select Columns Distplot :",all_columns_names)
st.write(dataframe.show_displot(df[selected_columns_names]))
st.pyplot()
# boxplot
if option3==listy3[3]:
all_columns_names = dataframe.show_columns(numer_df)
selected_columns_names = st.selectbox("Select Column for Boxplot :",all_columns_names)
st.write(dataframe.show_boxplt(df[selected_columns_names]))
st.pyplot()
# Bivariate plotting
Scatter1 = dataframe.show_columns(numer_df)
Scatter2 = dataframe.show_columns(numer_df)
listy5 = ['Select','Scatter plot','Bar plot']
option5 = st.sidebar.selectbox('Bivariate Analysis',listy5)
if option5==listy5[1]:
Scatter11 = st.selectbox("Select Column 1 For Scatter Plot (Numerical Columns)",Scatter1)
Scatter22 = st.selectbox("Select Column 2 For Scatter Plot (Numerical Columns)",Scatter2)
st.write(dataframe.show_scatterplt(df,Scatter11,Scatter22))
if option5==listy5[2]:
Scatter11 = st.selectbox("Select Column 1 For Bar Plot ",Scatter1)
Scatter22 = st.selectbox("Select Column 2 For Bar Plot ",Scatter2)
st.write(dataframe.show_bar(df,Scatter11,Scatter22))
listy4 = ['Select','Heatmap']
option4 = st.sidebar.selectbox('Multivariate Analysis',listy4)
# heatmap
if option4==listy4[1]:
st.write(dataframe.show_heatmp(df.corr()))
st.pyplot()
# if(option1 != listy1[0] or option2 != listy2[0] or option3 != listy3[0] or option4 != listy4[0] or option5 != listy5[0]):
# success_txt.empty()
# st.sidebar.download_button(
# label="Download Complete Report",
# data=df.to_csv(),
# file_name='CSV_Report.pdf',
# mime='text/pdf'
# )
if __name__ == '__main__':
load = DataFrame_Loader()
dataframe = EDA_Dataframe_Analysis()
info = Attribute_Information()
main()