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README.md

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+# assignment-2-Antelope270
+assignment-2-Antelope270 created by GitHub Classroom
+### 1. What is included in the assigment?
+- A python file named final app.py
+- A screencast recoding video of the app
+- A spotify song dataset: spotify_dataset.csv
+- A image of spotify logo: spotify.gif
+- A README.md file which explain the description of the goals, rationale for my design decisions and overview of my development process.
+
+### 2. What is the goal of this app?
+Use the dataset Spotify Top 200 Charts (2020-2021) to: 
+- Provide users a interactive line chart to display explorable data relationships between Audio features (Danceability, Acousticness, Energy, Valence)
+- Provide users interactive sliders to select value of Danceability, Acousticness, Energy, Valence to select user's desirable songs.
+
+### 3. Overview of my development process
+- Import the data set and build a title
+- Clean the data (some of the columns have NaN and ' ')
+- Adjust the data types (to string or float)
+- Reset the index of data
+- Make relationship line chart between song features and popularity 
+  -  Make a selectbox between song features: Danceability, Acousticness, Energy, Valence
+  -  Draw 4 song featured line charts for selection outcome
+- Make sliders filter
+  - Draw 4 sliders for dragging
+  - Display the filtered song table  

final app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+from pandas.core.base import DataError
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import altair as alt
+
+
+
+Image = Image.open('C:\\Users\\Antelope Cai\\Desktop\\spotify.gif')
+st.image(Image,width=150)
+st.title('Guess your favorite song!')
+st.caption('The dataset is from Kaggle, 2020-2021')
+
+
+def load_data():
+    data = pd.read_csv('C:\\Users\\Antelope Cai\\Desktop\\CMU\\IDS\\spotify_dataset.csv')
+    return data
+
+
+data = load_data()
+df = pd.DataFrame(data,columns=(['Song Name','Popularity','Release Date','Artist','Danceability','Acousticness','Energy','Valence']))
+df= df.astype('string')
+song=df[~df['Energy'].isin([' '])]
+song['Energy']=song['Energy'].astype(float)
+song['Acousticness']=song['Acousticness'].astype(float)
+song['Valence']=song['Valence'].astype(float)
+song['Danceability']=song['Danceability'].astype(float)
+song['Popularity']=song['Popularity'].astype(float)
+song.reset_index(drop=True, inplace=True)
+
+# st.dataframe(song)
+
+# data type test
+# new_song=song.rename(columns = {
+#     col : f"{col} ({dtype})"
+#     for col, dtype in song.dtypes.to_dict().items()
+# }).head()
+
+# st.dataframe(new_song)
+
+#selector filter
+st.markdown("### 1. What is the relationship between song features and popularity?")
+option = st.selectbox(
+     'Select a audio feature you want to see the relationship bewteen it and popularity?',
+     ('Danceability', 'Acousticness', 'Energy', 'Valence'))
+
+st.write('You selected:', option)
+
+
+# Basic Altair line chart changed from selection
+st.markdown("#### Outcome: line chart")
+basic_chart = alt.Chart(song).mark_line().encode(
+    x='Popularity',
+    y=option,
+).properties(
+    width=700,
+    height=400
+).configure_mark(
+    color='green'
+)
+st.altair_chart(basic_chart)
+
+#slider filter
+st.markdown("### 2. Use the song feature sliders to filter your favorite song!")
+dance_min = min(song['Danceability'])
+dance_max = max(song['Danceability'])
+
+acoust_min= min(song['Acousticness'])
+acoust_max= max(song['Acousticness'])
+
+energy_min = min(song['Energy'])
+energy_max = max(song['Energy'])
+
+valence_min = min(song['Valence'])
+valence_max = max(song['Valence'])
+
+
+sliders = {
+    "Danceability": st.slider(
+        "What is your desired Danceability?", dance_min, dance_max, 0.56
+    ),
+
+    "Acousticness":st.slider(
+        "What is your desired Acousticness?", acoust_min, acoust_max, 0.495
+    ),
+
+    "Energy":st.slider(
+        "What is your desired Energy?", energy_min, energy_max, 0.51
+    ),
+    "Valence":st.slider(
+        "What is your desired Valence?", valence_min, valence_max, 0.50
+    ),
+
+}
+
+filter = np.full(1545, True)  # Initialize filter as only True
+
+st.markdown("#### Outcome: Filtered Spotify song table")
+for feature_name, slider in sliders.items():
+    # Here we update the filter to take into account the value of each slider
+    filter = (
+        filter
+         & (song[feature_name] >= slider)
+    )
+filter.reset_index(drop=True, inplace=True)
+
+st.write(song[filter])
+
+with st.sidebar:
+    st.header('Appendix')
+    with st.expander("Spotify Audio Feature definitions"):
+            st.subheader("Danceability")
+            st.markdown("Describes how suitable a track is for dancing based on a "+
+                    "combination of musical elements including tempo, rhythm stability, beat strength, "+
+                    "and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.")
+
+            st.subheader("Acousticness")
+            st.markdown("A confidence measure from 0.0 to 1.0 of whether the "+
+                    "track is acoustic. 1.0 represents high confidence the track is acoustic.")
+
+            st.subheader("Energy")
+            st.markdown("Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity "+
+                    "and activity. Typically, energetic tracks feel fast, loud, and noisy."+
+                    "For example, death metal has high energy, while a Bach prelude scores low on the scale. "+
+                    "Perceptual features contributing to this attribute include dynamic range, perceived loudness, "+
+                    "timbre, onset rate, and general entropy.")
+
+            st.subheader("Valence")
+            st.markdown("A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. "+
+            "Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low"+
+            "valence sound more negative (e.g. sad, depressed, angry).")