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# importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import streamlit as st
# Title and Markdown
st.title("AN EXAMPLE EDA APP")
st.markdown(''' <h3>This is an example of how to do EDA in streamlit app</h3>''',unsafe_allow_html=True)
# File upload
file_up = st.file_uploader("Upload a file", type='csv')
# Check if the file uploaded is successfull or not, if successfull then read the file
if file_up is not None:
st.success("File uploaded successfully")
df = pd.read_csv(file_up)
obj = []
int_float = []
for i in df.columns:
clas = df[i].dtypes
if clas == 'object':
obj.append(i)
else:
int_float.append(i)
# Remove null values and replace them with mean and median value
with st.form(key='my_form'):
with st.sidebar:
st.sidebar.header("To remove NULL values press below button")
submit_button = st.form_submit_button(label="Remove NULL")
if submit_button:
for i in df.columns:
clas = df[i].dtypes
if clas == 'object':
df[i].fillna(df[i].mode()[0], inplace = True)
else:
df[i].fillna(df[i].mean(), inplace = True)
# finding the number of null values in each column
ls = []
for i in df.columns:
dd = sum(pd.isnull(df[i]))
ls.append(dd)
# if number of null values are zero it will display some text else it will plot bar plot by each column
if max(ls) == 0:
st.write("Total no. of NULL values: ", str(max(ls)))
else:
st.write("Bar plot to know the number of NULL values in each column")
st.write("Total number of null values: ", str(max(ls)))
fig = px.bar(x=df.columns, y=ls,labels={'x':"Column Names",'y':"No. of Null values"})
st.plotly_chart(fig)
# Frequency Plot
st.sidebar.header("Select variable")
selected = st.sidebar.selectbox('Object variables',obj)
st.write("Bar Plot to know the frequency of each category")
frequency = df[selected].value_counts()
fig2 = px.bar(frequency, x=frequency.index,y=selected,labels={'x':selected, 'y':'count'})
st.plotly_chart(fig2)
# Correlation chart
st.sidebar.header("Select variable")
selected2 = st.sidebar.multiselect("Variables",int_float)
st.write("Scatter plot for correlation")
if len(selected2) == 2:
fig3 = px.scatter(df,x=selected2[0], y=selected2[1])
st.plotly_chart(fig3)
else:
st.write("Select any 2 variables only") |