Spaces:
Sleeping
Sleeping
harrychangjr
commited on
Commit
•
b399bc7
1
Parent(s):
30c9e7b
analysis overview
Browse files- .DS_Store +0 -0
- Home.py +2 -2
- gaming/Last 28 days.xlsx +0 -0
- gaming/Last 60 days.xlsx +0 -0
- gaming/Last 7 days.xlsx +0 -0
- gaming/Total followers.xlsx +0 -0
- gaming/Trending videos.xlsx +0 -0
- gaming/Video Posts.xlsx +0 -0
- pages/04_Case Study:_Gaming_Clips.py +279 -0
.DS_Store
ADDED
Binary file (8.2 kB). View file
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Home.py
CHANGED
@@ -15,9 +15,9 @@ import streamlit as st
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# add_page_title()
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# Set page title
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st.set_page_config(page_title="
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st.header("
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# add_page_title()
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# Set page title
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st.set_page_config(page_title="Enhanced TikTok Analytics Dashboard", page_icon = "📊", layout = "centered", initial_sidebar_state = "auto")
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st.header("Enhanced TikTok Analytics Dashboard")
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gaming/Last 28 days.xlsx
ADDED
Binary file (19.6 kB). View file
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gaming/Last 60 days.xlsx
ADDED
Binary file (25.1 kB). View file
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gaming/Last 7 days.xlsx
ADDED
Binary file (15.9 kB). View file
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gaming/Total followers.xlsx
ADDED
Binary file (20.9 kB). View file
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gaming/Trending videos.xlsx
ADDED
Binary file (19.8 kB). View file
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gaming/Video Posts.xlsx
ADDED
Binary file (12.5 kB). View file
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pages/04_Case Study:_Gaming_Clips.py
ADDED
@@ -0,0 +1,279 @@
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import streamlit as st
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import pandas as pd
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import numpy as np
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import datetime
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import plotly.express as px
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import plotly.graph_objects as go
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import statsmodels.api as sm
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from millify import millify
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import r2_score
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from st_aggrid import AgGrid
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import io
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import re
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import emoji
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from collections import Counter
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import openpyxl
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from gensim.models import Word2Vec
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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from xgboost import XGBRegressor
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from sklearn.model_selection import train_test_split
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import seaborn as sns
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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# Set page title
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st.set_page_config(page_title="Case Study: Gaming Clips - Tiktok Analytics Dashboard", page_icon = "📊", layout = "centered", initial_sidebar_state = "auto")
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st.header("Case Study: Gaming Clips")
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selected_options = ["Background Information", "Uploaded Datasets", "Analysis"]
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selected = st.selectbox("Which section would you like to read?", options = selected_options)
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st.write("Current selection:", selected)
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if selected == "Background Information":
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st.subheader("Background Information")
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elif selected == "Uploaded Datasets":
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st.subheader("Uploaded Datasets")
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last7days = pd.read_excel('gaming/Last 7 days.xlsx')
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last28days = pd.read_excel('gaming/Last 28 days.xlsx')
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last60days = pd.read_excel('gaming/Last 60 days.xlsx')
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totalfollowers = pd.read_excel('gaming/Total followers.xlsx')
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trendingvideos = pd.read_excel('gaming/Trending videos.xlsx')
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videoposts = pd.read_excel('gaming/Video Posts.xlsx')
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#function to convert any dataframe to a csv file
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@st.cache_data
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def convert_df(df):
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# IMPORTANT: Cache the conversion to prevent computation on every rerun
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return df.to_csv().encode('utf-8')
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st.write('Last 7 days.xlsx')
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st.write(last7days)
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#converting the sample dataframe
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csv = convert_df(last7days)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Last 7 days.csv',
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mime='text/csv',
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)
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st.write('Last 28 days.xlsx')
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st.write(last28days)
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#converting the sample dataframe
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csv = convert_df(last28days)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Last 28 days.csv',
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mime='text/csv',
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)
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st.write('Last 60 days.xlsx')
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st.write(last60days)
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#converting the sample dataframe
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csv = convert_df(last60days)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Last 60 days.csv',
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mime='text/csv',
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)
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st.write('Total followers.xlsx')
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st.write(totalfollowers)
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#converting the sample dataframe
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csv = convert_df(totalfollowers)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Total followers.csv',
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mime='text/csv',
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)
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st.write('Trending videos.xlsx')
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st.write(trendingvideos)
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#converting the sample dataframe
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csv = convert_df(trendingvideos)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Trending videos.csv',
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mime='text/csv',
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)
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st.write('Video Posts.xlsx')
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st.write(videoposts)
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#converting the sample dataframe
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csv = convert_df(videoposts)
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#adding a download button to download csv file
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st.download_button(
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label="Download data as CSV",
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data=csv,
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file_name='Video Posts.csv',
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mime='text/csv',
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)
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elif selected == "Analysis":
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def plot_chart(data, chart_type, x_var, y_var, z_var=None, show_regression_line=False, show_r_squared=False):
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scatter_marker_color = 'green'
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regression_line_color = 'red'
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if chart_type == "line":
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fig = px.line(data, x=x_var, y=y_var)
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elif chart_type == "bar":
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fig = px.bar(data, x=x_var, y=y_var)
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elif chart_type == "scatter":
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fig = px.scatter(data, x=x_var, y=y_var, color_discrete_sequence=[scatter_marker_color])
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if show_regression_line and x_var != 'Date':
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X = data[x_var].values.reshape(-1, 1)
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y = data[y_var].values.reshape(-1, 1)
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model = LinearRegression().fit(X, y)
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y_pred = model.predict(X)
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r_squared = r2_score(y, y_pred) # Calculate R-squared value
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fig.add_trace(
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go.Scatter(x=data[x_var], y=y_pred[:, 0], mode='lines', name='Regression Line', line=dict(color=regression_line_color))
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)
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# Add R-squared value as a text annotation
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fig.add_annotation(
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x=data[x_var].max(),
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y=y_pred[-1, 0],
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text=f"R-squared: {r_squared:.4f}",
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showarrow=False,
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font=dict(size=14),
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bgcolor='rgba(255, 255, 255, 0.8)',
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bordercolor='black',
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borderwidth=1,
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borderpad=4
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)
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elif chart_type == "heatmap":
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fig = px.imshow(data, color_continuous_scale='Inferno')
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elif chart_type == "scatter_3d":
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if z_var is not None:
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fig = px.scatter_3d(data, x=x_var, y=y_var, z=z_var, color=data.columns[0])
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else:
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st.warning("Please select Z variable for 3D line plot.")
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return
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elif chart_type == "line_3d":
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if z_var is not None:
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fig = go.Figure(data=[go.Scatter3d(x=data[x_var], y=data[y_var], z=data[z_var], mode='lines')])
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fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var)) # Set the axis name
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else:
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st.warning("Please select Z variable for 3D line plot.")
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return
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elif chart_type == "surface_3d":
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if z_var is not None:
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fig = go.Figure(data=[go.Surface(z=data.values)])
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fig.update_layout(scene=dict(xaxis_title=x_var, yaxis_title=y_var, zaxis_title=z_var)) # Set the axis name
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else:
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st.warning("Please select Z variable for 3D line plot.")
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return
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elif chart_type == "radar":
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fig = go.Figure()
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for col in data.columns[1:]:
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fig.add_trace(go.Scatterpolar(r=data[col], theta=data[x_var], mode='lines', name=col))
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fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[data[data.columns[1:]].min().min(), data[data.columns[1:]].max().max()])))
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st.plotly_chart(fig)
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def plot_radar_chart(data, columns):
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df = data[columns]
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fig = go.Figure()
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for i in range(len(df)):
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date_label = data.loc[i, 'Date']
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fig.add_trace(go.Scatterpolar(
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r=df.loc[i].values,
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theta=df.columns,
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fill='toself',
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name=date_label
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))
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209 |
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fig.update_layout(
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polar=dict(
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212 |
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radialaxis=dict(
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213 |
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visible=True,
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214 |
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range=[0, df.max().max()]
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)
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216 |
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),
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217 |
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showlegend=True
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218 |
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)
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219 |
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220 |
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st.plotly_chart(fig)
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221 |
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st.subheader("Analysis")
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222 |
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taba, tabb, tabc = st.tabs(["Overview", "Content", "Followers"])
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223 |
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with taba:
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224 |
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st.write("**Overview**")
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225 |
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226 |
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data = pd.read_excel('gaming/Last 7 days.xlsx')
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227 |
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228 |
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x_var = st.sidebar.selectbox("Select X variable for Last 7 days", data.columns)
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229 |
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y_var = st.sidebar.selectbox("Select Y variable for Last 7 days", data.columns)
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230 |
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show_regression_line = False
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231 |
+
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232 |
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z_var_options = ["None"] + list(data.columns)
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233 |
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z_var = st.sidebar.selectbox("Select Z variable for 3D charts (if applicable)", z_var_options)
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234 |
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tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8 = st.tabs(["Line", "Bar", "Scatterplot", "Heatmap",
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"3D Scatterplot", "3D Lineplot", "3D Surfaceplot", "Radar chart"])
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with tab1:
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st.write("Lineplot for 'Last 7 days'")
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plot_chart(data, "line", x_var, y_var)
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with tab2:
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st.write("Barplot for 'Last 7 days'")
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plot_chart(data, "bar", x_var, y_var)
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with tab3:
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st.write("Scatterplot for 'Last 7 days'")
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247 |
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show_regression_line = st.checkbox("Show regression line for Last 7 days scatterplot (does not apply when X = Date)")
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248 |
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plot_chart(data, "scatter", x_var, y_var, show_regression_line=show_regression_line)
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249 |
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250 |
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with tab4:
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st.write("Heatmap for 'Last 7 days'")
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plot_chart(data, "heatmap", x_var, y_var)
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with tab5:
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st.write("3D Scatterplot for 'Last 7 days'")
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if z_var != "None":
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plot_chart(data, "scatter_3d", x_var, y_var, z_var)
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with tab6:
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st.write("3D Lineplot for 'Last 7 days'")
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if z_var != "None":
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plot_chart(data, "line_3d", x_var, y_var, z_var)
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263 |
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264 |
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with tab7:
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st.write("3D Surfaceplot for 'Last 7 days'")
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if z_var != "None":
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plot_chart(data, "surface_3d", x_var, y_var, z_var)
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268 |
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269 |
+
with tab8:
|
270 |
+
st.write("Radar chart for 'Last 60 days'")
|
271 |
+
radar_columns = ['Video views', 'Profile views', 'Likes', 'Comments', 'Shares']
|
272 |
+
plot_radar_chart(data, radar_columns)
|
273 |
+
# Add more conditions for other specific file names if needed
|
274 |
+
|
275 |
+
with tabb:
|
276 |
+
st.write("**Content**")
|
277 |
+
|
278 |
+
with tabc:
|
279 |
+
st.write("**Followers**")
|