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Adding solutions to episode 5
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@wood-chris
wood-chris committed Feb 28, 2024
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Expand Up @@ -173,6 +173,23 @@ a = [1, 2, 3, 4, 5]
> a[len(a)]
> ~~~
> {: .language-python }
>> ## Solution
>> 1. `a[0]`` returns 1, as Python starts with element 0 (this may be different from what
>> you have previously experience with other languages e.g. MATLAB and R)
>> 2. `a[5]` raises an IndexError
>> 3. The error is raised because the list a has no element with index 5: it has only five entries, indexed from 0 to 4.
>> 4. `a[len(a)]` also raises an IndexError. `len(a)` returns 5, making `a[len(a)]` equivalent to `a[5]`.
>> To retreive the final element of a list, use the index -1, e.g.
>> ~~~
>> a[-5]
>> ~~~
>> {: .language-python}
>>
>> ~~~
>> 5
>> ~~~
>> {: .output}
> {: .solution}
{: .challenge}
Expand Down Expand Up @@ -419,20 +436,57 @@ arrays)
>
> 1. What happens when you execute:
>
> - `waves_df[0:1]`
> - `waves_df[0:3]`
> - `waves_df[0]`
> - `waves_df[:4]`
> - `waves_df[:-1]`
>
> 2. What happens when you call:
>
> - `waves_df.iloc[0:1]`
> - `waves_df.iloc[0:3]`
> - `waves_df.iloc[0]`
> - `waves_df.iloc[:4, :]`
> - `waves_df.iloc[0:4, 1:4]`
> - `waves_df.loc[0:4, 1:4]`
>
> - How are the last two commands different?
>> ## Solution
>>
>> 1.
>> - `waves_df[0:3]` returns the first three rows of the DataFrame:
>> ~~~
>> record_id buoy_id Name Date Tz ... Temperature Spread Operations Seastate Quadrant
>> 0 1 14 SW Isles of Scilly WaveNet Site 17/04/2023 00:00 7.2 ... 10.8 26.0 crew swell west
>> 1 2 7 Hayling Island Waverider 17/04/2023 00:00 4.0 ... 10.2 14.0 crew swell south
>> 2 3 5 Firth of Forth WaveNet Site 17/04/2023 00:00 3.7 ... 7.8 28.0 crew windsea east
>> [3 rows x 13 columns]
>> ~~~
>> {: .output}
>>
>> - `waves_df[0]` results in a ‘KeyError’, since direct indexing of a row is redundant this way - `iloc` should be used instead (`waves_df[0:1]` could be used to obtain only the first row using this notation)
>>
>> - `waves_df[:4]` slices from the first row to the fourth:
>>
>> ~~~
>> record_id buoy_id Name Date Tz ... Temperature Spread Operations Seastate Quadrant
>> 0 1 14 SW Isles of Scilly WaveNet Site 17/04/2023 00:00 7.2 ... 10.8 26.0 crew swell west
>> 1 2 7 Hayling Island Waverider 17/04/2023 00:00 4.0 ... 10.2 14.0 crew swell south
>> 2 3 5 Firth of Forth WaveNet Site 17/04/2023 00:00 3.7 ... 7.8 28.0 crew windsea east
>> 3 4 3 Chesil Waverider 17/04/2023 00:00 5.5 ... 10.2 48.0 crew swell south
>> ~~~
>> {: .output}
>>
>> - `waves_df[:-1]` provides everything except the final row of a DataFrame. You can use negative index numbers to count backwards from the last entry.
>>
>> 2.
>> - `waves_df.iloc[0:1]` returns the first row
>> - `waves_df.iloc[0]` returns the first row as a named list
>> - `waves_df.iloc[:4, :]` returns all columns of the first four rows
>> - `waves_df.iloc[0:4, 1:4]` selects specified columns of the first four rows
>> - `waves_df.loc[0:4, 1:4]` results in a 'TypeError' - see below.
>>
>> 3. While iloc uses integers as indices and slices accordingly, loc works with labels. It is like accessing values from a dictionary, asking for the key names. Column names 1:4 do not exist, so the call to `loc` above results in an error. Check also the difference between `waves_df.loc[0:4]` and `waves_df.iloc[0:4]`.
> {: .solution}
{: .challenge}
Expand Down Expand Up @@ -533,9 +587,9 @@ Experiment with selecting various subsets of the "waves" data.
> ## Challenge - Queries
>
> 1. Select a subset of rows in the `waves_df` DataFrame that contain data from
> the year 2022 and that contain Temperature values less than or equal to 8. How
> many rows did you end up with? You may want to create a new column containing the dates
> formatted as DateType that we created earlier
> the year 2023 and that contain Temperature values less than or equal to 8. How
> many rows did you end up with? Tip #1: You can't access attributes of a DateTme objects stored in a Series directly!
> Tip #2: you may want to create a new column containing the dates formatted as DateType that we created earlier!
>
> 2. You can use the `isin` command in Python to query a DataFrame based upon a
> list of values as follows:
Expand All @@ -548,16 +602,108 @@ Experiment with selecting various subsets of the "waves" data.
> Use the `isin` function to find all plots that contain buoy ids 5 and 7
> in the "waves" DataFrame. How many records contain these values?
>
> 3. Experiment with other queries. Create a query that finds all rows with a
> 3. Experiment with other queries. e.g. Create a query that finds all rows with a
> Tpeak greater than or equal to 10.
>
> 4. The `~` symbol in Python can be used to return the OPPOSITE of the
> selection that you specify in Python. It is equivalent to **is not in**.
> Write a query that selects all rows with Quadrant NOT equal to 'south' or 'east' in
> the "waves" data.
>
>> ## Solution
>> 1. This is possible in one-line:
>> ~~~
>> waves_df[(pd.to_datetime(waves_df.Date, format="%d/%m/%Y %H:%M").dt.year == 2023) & (waves_df["Temperature"] <= 8)]
>> ~~~
>> {: .language-python}
>>
>> First, we convert the `Date` column to objects of type `Timestamp`, then use the `dt` _accessor object_ to get information about the dates. A `series` isn't a `Timestamp`, so we can't use the `Timestamp` attributes directly
>> If we wanted to save just the Year in a new column, we could do:
>> ~~~
>> timestamps = pd.to_datetime(waves_df.Date, format="%d/%m/%Y %H:%M")
>> years = timestamps.dt.year
>> waves_df["Year'] = years
>> waves_df[(waves_df.Year == 2023) & (waves_df.Temperature <=8)]
>> ~~~
>> {: .language-python}
>>
>> And then we can see there are 2 rows which match this condition (don't forget we can also use the `len` function)
>>
>> ~~~
>> record_id buoy_id Name Date Tz Peak Direction ... Temperature Spread Operations Seastate Quadrant Year
>> 2 3 5 Firth of Forth WaveNet Site 17/04/2023 00:00 3.7 115.0 ... 7.80 28.0 crew windsea east 2023
>> 9 10 5 Firth of Forth WaveNet Site 15/04/2023 00:00 3.2 124.0 ... 7.35 23.0 crew windsea east 2023
>>
>> [2 rows x 14 columns]
>> ~~~
>> {: .output}
>>
>> 2.
>> ~~~
>> waves_df[waves_df['buoy_id'].isin([5,7])]
>> ~~~
>> {: .language-python}
>>
>> ~~~
>> record_id buoy_id Name Date Tz ... Spread Operations Seastate Quadrant Year
>> 1 2 7 Hayling Island Waverider 17/04/2023 00:00 4.0 ... 14.0 crew swell south 2023
>> 2 3 5 Firth of Forth WaveNet Site 17/04/2023 00:00 3.7 ... 28.0 crew windsea east 2023
>> 8 9 7 Hayling Island Waverider 15/04/2023 00:00 3.7 ... 31.0 crew windsea east 2023
>> 9 10 5 Firth of Forth WaveNet Site 15/04/2023 00:00 3.2 ... 23.0 crew windsea east 2023
>> 1071 1072 5 Firth of Forth WaveNet Site 16/02/2009 11:00 3.0 ... 30.0 crew windsea west 2009
>> ... ... ... ... ... ... ... ... ... ... ... ...
>> 1350 1351 5 Firth of Forth WaveNet Site 22/02/2009 06:30 2.6 ... 16.0 crew windsea west 2009
>> 1351 1352 5 Firth of Forth WaveNet Site 22/02/2009 07:00 2.7 ... 16.0 crew windsea west 2009
>> 1352 1353 5 Firth of Forth WaveNet Site 22/02/2009 07:30 2.7 ... 16.0 crew windsea west 2009
>> 1353 1354 5 Firth of Forth WaveNet Site 22/02/2009 08:00 2.7 ... 12.0 crew windsea west 2009
>> 1354 1355 5 Firth of Forth WaveNet Site 22/02/2009 08:30 2.8 ... 12.0 crew windsea west 2009
>>
>> [288 rows x 14 columns]
>> ~~~
>> {: .output}
>>
>> ~~~
>> len(waves_df[waves_df['buoy_id'].isin([5,7])])
>> ~~~
>> {: .language-python}
>>
>> ~~~
>> 5
>> ~~~
>> {: .output}
>>
>> 3.
>> ~~~
>> waves_df[waves_df['Tpeak'] >= 10]
>> ~~~
>> {: .language-python}
>>
>> 4.
>> ~~~
>> waves_df[~waves_df['Quadrant'].isin(['south','east'])]
>> ~~~
>> {: .language-python}
>>
>> ~~~
>> record_id buoy_id Name Date Tz ... Spread Operations Seastate Quadrant Year
>> 0 1 14 SW Isles of Scilly WaveNet Site 17/04/2023 00:00 7.2 ... 26.0 crew swell west 2023
>> 4 5 10 M6 Buoy 17/04/2023 00:00 7.6 ... 89.0 no go swell west 2023
>> 5 6 9 Lomond 17/04/2023 00:00 4.0 ... NaN crew swell north 2023
>> 6 7 2 Cardigan Bay 17/04/2023 00:00 5.9 ... 18.0 crew swell west 2023
>> 7 8 14 SW Isles of Scilly WaveNet Site 15/04/2023 00:00 7.2 ... 18.0 crew swell west 2023
>> ... ... ... ... ... ... ... ... ... ... ... ...
>> 2068 2069 16 west of Hebrides 18/10/2022 16:00 6.1 ... 28.0 crew swell north 2022
>> 2069 2070 16 west of Hebrides 18/10/2022 16:30 5.9 ... 34.0 crew swell north 2022
>> 2070 2071 16 west of Hebrides 18/10/2022 17:00 5.6 ... 34.0 crew swell north 2022
>> 2071 2072 16 west of Hebrides 18/10/2022 17:30 5.7 ... 31.0 crew swell north 2022
>> 2072 2073 16 west of Hebrides 18/10/2022 18:00 5.7 ... 34.0 crew swell north 2022
>>
>> [1985 rows x 14 columns]
>> ~~~
>> {: .output}
> {: .solution}
{: .challenge}
# Using masks to identify a specific condition
A **mask** can be useful to locate where a particular subset of values exist or
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