Jira Stats
----------

Process CSV file exported from JIRA filter with all columns.

::

  import streamlit as st
  import pandas as pd
  import os

  csv_files = [f for f in os.listdir('.') if f.endswith('.csv')]
  csv_files.sort()
  csv_file = st.sidebar.radio("Select exported JIRA filter CSV file to process:", csv_files)

  df = pd.read_csv(csv_file)

  def drop_columns_from_list():
      with open("drop_columns.txt", "r") as f:
          return [line.strip() for line in f]

  def drop_columns_by_prefix():
      prefixes = [
          'Watchers Id',
          'Watchers',
          'Log Work',
          'Attachment',
          'Comment',
          'Components',
          'Labels',
          'Sprint',
      ]
      cols_to_drop = [col for col in df.columns if any(col.startswith(prefix) for prefix in prefixes)]
      # st.write(cols_to_drop)
      df.drop(columns=cols_to_drop, inplace=True)

  drop_columns_by_prefix()
  # df.drop(columns=drop_columns_from_list(), inplace=True)

  st.sidebar.write("---")
  num_rows = df.shape[0]
  st.sidebar.write(f"Jiras: `{num_rows}`")

  total_time_spent = df['Time Spent'].sum()
  hours = round(total_time_spent/3600)
  st.sidebar.write(f"Total time spent: `{hours}` h")

Select row

::

  num = st.number_input("Row", min_value=0, max_value=num_rows-1)

Extract row to Series

::

  dfs = df.iloc[num]
  drops = dfs.dropna()
  st.write(f"Num cols: `{dfs.shape[0]}`, not empty: `{drops.shape[0]}`")

  # st.table(dfs)
  st.table(drops)