pages/1_📈_Plotting.py

import streamlit as st
import pandas as pd

st.header("Plotting Time Series Data")
st.markdown("Users can load their time-series data in **.csv** format and select a particular feature and plot-type.\
 Go ahead and use the sidebar on the left to upload your data files and to start visualizing it!")


def plot_collection(plot_only_this, collection, number_of_desired_plots=0):
  fig = go.Figure()
  plots_count = 0
  print("total number of graphs: ", len(collection))

  for (pattern_name, d) in collection:
    if pattern_name == plot_only_this:
      if plots_count ==0:
        fig.add_trace(go.Line(y=d))
        fig.update_layout(title=plot_only_this)
      else:
        fig.add_trace(go.Line(y=d))
      plots_count += 1
      fig.update_layout(width=1200, height=800)
      if number_of_desired_plots:
        if plots_count == number_of_desired_plots:
          break
            

with st.sidebar:
    plot = st.radio("Select the kind of visualization:",('Plot feature collection', 'Compare users', 'Plot distribution'))
    file = st.file_uploader("Load CSV file", accept_multiple_files = False)
    if file:
        df = pd.read_csv(file, index_col = False)
        # df.index = df['Unnamed: 0'].tolist()
        del df['Unnamed: 0']
        if 'df' not in st.session_state:
                st.session_state['df'] = df
        st.success("Your data has been successfully loaded! 🤗")

if 'df' in list(st.session_state.keys()):
    st.markdown("Your uploaded data:") 
    st.dataframe(st.session_state.df)
else:
    st.caption("Upload your data using the sidebar and select a plot-type to start :sunglasses:")

df_base = st.session_state.df if 'df' in list(st.session_state.keys()) else pd.DataFrame()
n = len(df_base)
col1, col2 = st.columns(2)
with col1:
    if n:
        st.info(f"Your data has {n} samples.")
        slider_range = list(range(n))
        n_plot = st.slider("How many samples do you want to plot in the same graph", slider_range[0]+1, slider_range[-1]+1, 5)
        st.write(f"Action: {plot} using {n_plot} samples")
        st.button("Plot now! 📊")

if not df_base.empty:
    st.warning("Consider running outlier detection to clean your data!", icon="⚠️")