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# Sort

Another simple but common technique for analyzing data is sorting. This can be useful for ranking the DataFrame to show the first and last members of the table according to a particular column.

```{code-cell}
:tags: [hide-cell]

import pandas as pd
accident_list = pd.read_csv("https://raw.githubusercontent.com/palewire/first-python-notebook/main/docs/src/_static/ntsb-accidents.csv")
accident_list['latimes_make_and_model'] = accident_list['latimes_make_and_model'].str.upper()
accident_counts = accident_list.groupby("latimes_make_and_model").size().reset_index().rename(columns={0: "accidents"})
survey = pd.read_csv("https://raw.githubusercontent.com/palewire/first-python-notebook/main/docs/src/_static/faa-survey.csv")
survey['latimes_make_and_model'] = survey['latimes_make_and_model'].str.upper()
merged_list = pd.merge(accident_counts, survey, on="latimes_make_and_model")
merged_list['per_hour'] = merged_list.accidents / merged_list.total_hours
merged_list['per_100k_hours'] = (merged_list.accidents / merged_list.total_hours) * 100_000
```

The [`sort_values`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html) method is how pandas does it. It expects that you provide it with the name of the column to sort by in quotes. Try sorting by our computed field.

```{code-cell}
:tags: [show-input]
merged_list.sort_values("per_100k_hours")
```

Note that returns the DataFrame resorted in ascending order from lowest to highest. That is pandas’ default way of sorting. You reverse it to show the largest values first by passing in an optional keyword argument called `ascending`. When it is set to `False`, the DataFrame is sorted in descending order.

```{code-cell}
:tags: [show-input]
merged_list.sort_values("per_100k_hours", ascending=False)
```

Congratulations. With that, you've recreated the heart of the analysis published in the Los Angeles Times and covered most of the basic skills necessary to access and analyze data with pandas. In our next chapter, we will show another pandas trick that's sure to come in handy.