Upload EDAapp.py

EDAapp.py

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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")