Create a session bar chart

# Table: Sessions
# 
# +---------------------+---------+
# | Column Name         | Type    |
# +---------------------+---------+
# | session_id          | int     |
# | duration            | int     |
# +---------------------+---------+
# session_id is the column of unique values for this table.
# duration is the time in seconds that a user has visited the application.
# 
#  
# 
# You want to know how long a user visits your application. You decided to create bins of "[0-5>", "[5-10>", "[10-15>", and "15 minutes or more" and count the number of sessions on it.
# 
# Write a solution to report the (bin, total).
# 
# Return the result table in any order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Sessions table:
# +-------------+---------------+
# | session_id  | duration      |
# +-------------+---------------+
# | 1           | 30            |
# | 2           | 199           |
# | 3           | 299           |
# | 4           | 580           |
# | 5           | 1000          |
# +-------------+---------------+
# Output:
# +--------------+--------------+
# | bin          | total        |
# +--------------+--------------+
# | [0-5>        | 3            |
# | [5-10>       | 1            |
# | [10-15>      | 0            |
# | 15 or more   | 1            |
# +--------------+--------------+
# Explanation:
# For session_id 1, 2, and 3 have a duration greater or equal than 0 minutes and less than 5 minutes.
# For session_id 4 has a duration greater or equal than 5 minutes and less than 10 minutes.
# There is no session with a duration greater than or equal to 10 minutes and less than 15 minutes.
# For session_id 5 has a duration greater than or equal to 15 minutes.

import pandas as pd

def create_bar_chart(sessions: pd.DataFrame) -> pd.DataFrame:
  bins = ['[0-5>', '[5-10>', '[10-15>', '15 or more']
  conditions = [
    sessions['duration'] < 300,
    (sessions['duration'] >= 300) & (sessions['duration'] < 600),
    (sessions['duration'] >= 600) & (sessions['duration'] < 900),
    sessions['duration'] >= 900
  ]
  import numpy as np
  sessions['bin'] = np.select(conditions, bins, default='')
  result = sessions.groupby('bin').size().reset_index(name='total')
  all_bins = pd.DataFrame({'bin': bins})
  result = all_bins.merge(result, on='bin', how='left')
  result['total'] = result['total'].fillna(0).astype(int)
  return result