IPYNB to PY Converter

#!/usr/bin/env python
# coding: utf-8

# # Bike rides and rain: a short walkthrough
# 
# A week of **made-up** numbers from a small town's bike-share scheme. It shows what a notebook can hold: Markdown, code, a table, a chart, maths, a warning and, at the end, an error.

# In[1]:


import pandas as pd
import matplotlib.pyplot as plt


# In[2]:


rides = pd.DataFrame({
    "day": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
    "trips": [412, 438, 395, 451, 288, 230],
    "rain_mm": [0.0, 1.2, 4.5, 0.0, 9.8, 14.1],
}).set_index("day")
rides


# ## How busy was the week?
# 
# The mean number of trips per day is
# 
# $$\bar{x} = \frac{1}{n}\sum_{i=1}^{n} x_i$$
# 
# where $n$ is the number of days and $x_i$ is the number of trips on day $i$.

# In[3]:


mean = rides["trips"].mean()
print(f"Mean trips per day: {mean:.1f}")
print(f"Busiest day: {rides['trips'].idxmax()}")


# In[4]:


import warnings

if rides["rain_mm"].max() > 10:
    warnings.warn("Saturday had over 10 mm of rain; the gauge reads high in storms.")


# ## Rain and riding
# 
# Fewer people rode on wet days. Pearson’s correlation coefficient measures how closely two columns move together, from −1 to 1:
# 
# $$r = \frac{\sum_i (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_i (x_i - \bar{x})^2}\,\sqrt{\sum_i (y_i - \bar{y})^2}}$$

# In[5]:


fig, ax = plt.subplots(figsize=(6.4, 3.6))
ax.bar(rides.index, rides["trips"], color="#4878d0")
ax.set_ylabel("Trips")
ax.set_title("Bike-share trips per day")
plt.show()


# In[6]:


round(rides["trips"].corr(rides["rain_mm"]), 3)


# ### A day that isn’t there
# 
# There is no Sunday in the data, so asking for it raises a `KeyError`. This is what a traceback looks like:

# In[7]:


rides.loc["Sun"]


# ### What next
# 
# - [x] Load a week of rides
# - [x] Chart them
# - [ ] Add the weekend’s weather from the town’s own gauge
# 
# > Made-up data, made for this example. Any resemblance to a real town is a coincidence.