IPYNB to Markdown Converter

# 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.

```python
import pandas as pd
import matplotlib.pyplot as plt
```

```python
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
```

<div>

<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right">
      <th></th>
      <th>trips</th>
      <th>rain_mm</th>
    </tr>
    <tr>
      <th>day</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Mon</th>
      <td>412</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>Tue</th>
      <td>438</td>
      <td>1.2</td>
    </tr>
    <tr>
      <th>Wed</th>
      <td>395</td>
      <td>4.5</td>
    </tr>
    <tr>
      <th>Thu</th>
      <td>451</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>Fri</th>
      <td>288</td>
      <td>9.8</td>
    </tr>
    <tr>
      <th>Sat</th>
      <td>230</td>
      <td>14.1</td>
    </tr>
  </tbody>
</table>
</div>

## 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 $i$.

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

```
Mean trips per day: 369.0
Busiest day: Thu
```

```python
import warnings

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

```
/tmp/ipykernel_4120/2715331896.py:4: UserWarning: Saturday had over 10 mm of rain; the gauge reads high in storms.
  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:

$$r = \frac{\sum_i (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_i (x_i - \bar{x})^2}\,\sqrt{\sum_i (y_i - \bar{y})^2}}$$

```python
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()
```

![Output of cell 8](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAFoBAMAAAA1HFdiAAAAAXNSR0IB2cksfwAAADBQTFRFAAAA//… (4 KB more on this line)

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

```
-0.979
```

### 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:

```python
rides.loc["Sun"]
```

```
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[7], line 1
----> 1 rides.loc["Sun"]

File ~/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3810     return self._engine.get_loc(casted_key)
   3811 except KeyError as err:
-> 3812     raise KeyError(key) from err

KeyError: '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.