10. Project - 7 (Case Study - 7) | Build Data Analysis Web App Using Python & Streamlit | Part 1
Project - 7 | Build Data Analysis Web App in Python - Streamlit
This video is added on the official Streamlit forum:
https://discuss.streamlit.io/t/weekly...
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App Link: https://data-analysis-web-app.herokua...
Machine Learning App: https://ml-web-app-pb.herokuapp.com/
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Link: https://streamlit.io/
Code :
Priyang Bhatt
Imports
import streamlit as st
import pandas as pd
import seaborn as sns
1. Title and Subheader
st.title("Data Analysis")
st.subheader("Data Analysis Using Python & Streamlit")
2. Upload Dataset
upload = st.file_uploader("Upload Your Dataset (In CSV Format)")
if upload is not None:
data=pd.read_csv(upload)
3. Show Dataset
if upload is not None:
if st.checkbox("Preview Dataset"):
if st.button("Head"):
st.write(data.head())
if st.button("Tail"):
st.write(data.tail())
4. Check DataType of Each Column
if upload is not None:
if st.checkbox("DataType of Each Column"):
st.text("DataTypes")
st.write(data.dtypes)
5. Find Shape of Our Dataset (Number of Rows And Number of Columns)
if upload is not None:
data_shape=st.radio("What Dimension Do You Want To Check?",('Rows',
'Columns'))
if data_shape=='Rows':
st.text("Number of Rows")
st.write(data.shape[0])
if data_shape=='Columns':
st.text("Number of Columns")
st.write(data.shape[1])
6. Find Null Values in The Dataset
if upload is not None:
test=data.isnull().values.any()
if test==True:
if st.checkbox("Null Values in the dataset"):
sns.heatmap(data.isnull())
st.pyplot()
else:
st.success("Congratulations!!!,No Missing Values")
7. Find Duplicate Values in the dataset
if upload is not None:
test=data.duplicated().any()
if test==True:
st.warning("This Dataset Contains Some Duplicate Values")
dup=st.selectbox("Do You Want to Remove Duplicate Values?", \
("Select One","Yes","No"))
if dup=="Yes":
data=data.drop_duplicates()
st.text("Duplicate Values are Removed")
if dup=="No":
st.text("Ok No Problem")
8. Get Overall Statistics
if upload is not None:
if st.checkbox("Summary of The Dataset"):
st.write(data.describe(include='all'))
9. About Section
if st.button("About App"):
st.text("Built With Streamlit")
st.text("Thanks To Streamlit")
10. By
if st.checkbox("By"):
st.success("Priyang Bhatt")
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download updated file
if st.button('Save DataFrame'):
open('data_streamlit.csv','w').write(data.to_csv())
st.text("Saved To local Drive")
Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. In just a few minutes you can build and deploy powerful data apps
Make sure that you have Python 3.6 - Python 3.8 installed.
Install Streamlit using PIP and run the ‘hello world’ app:
pip install streamlit
streamlit hello
Github Link: https://github.com/PRIYANG-BHATT/Data...
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