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Commit 7a028cd

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‎Streamlit/EDAapp.py‎

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

‎Streamlit/app5.py‎

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# ADDING A LOGO OR IMAGE TO YOUR APP
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import streamlit as st
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import pandas as pd
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from PIL import Image
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st.header("ADDING A LOGO OR IMAGE TO YOUR APP")
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image1 = Image.open("C:\\Users\\mithun\\OneDrive\\Pictures\\1_kgmImYxdhX01Yfk5qzkX-A.jpeg")
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image2 = Image.open("C:\\Users\\mithun\\OneDrive\\Pictures\\Saved Pictures\\Logo.png")
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st.image(image1, width=500)
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st.image(image2, width=500)
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# creating a button for uploading the files
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file_up = st.file_uploader("Upload a file", type='csv') # Display a file uploader widget. By default, uploaded files are limited to 200MB
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st.write(pd.DataFrame(file_up).head())

‎Streamlit/app6.py‎

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# SUBMIT BUTTON, SELECTION BUTTON AND SLIDERS
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from cProfile import label
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import pandas as pd
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import streamlit as st
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# select box/ drop down menu
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specalization = st.selectbox("Select the Specalization", ["Data Science","Artifical Intelligence","Software Engineering"])
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st.write("You have choosen the specalization: ", specalization)
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with st.form(key='my_form'):
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text_input = st.text_input(label="Enter your name")
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submit_button = st.form_submit_button(label='Submit')
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with st.form(key='new_form'):
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st.selectbox("Select the algorithm",['Logistic','Linear','SVM'], key=1)
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st.multiselect("Select the algorithm",['Logistic','Linear','SVM'], key=1)
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st.slider(label="Select the knowledge level", min_value=0,max_value=10, key=2)
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submit_button = st.form_submit_button(label='Submit')
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st.success("Submitted succesfully")

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