Week 4 Friday

Week 4 Friday#

Announcements#

Plan today: About 15 minutes to work on Worksheets/fill out your notecard/study for the midterm. Then lecture. Let me know what you want to review!
The midterm is Monday. A sample midterm and solutions are posted on the Week 5 page on Canvas.
Let me know if you still need a notecard.
The best way to study is to go over the worksheets, quizzes, and the sample midterm. Next priority would be the lecture notes.
I briefly mention lots of topics. If you’re not sure if something could appear on the midterm, ask on Ed Discussion and I’ll answer there.
Both Worksheets 7-8 are due Monday night (the usual schedule). Working on them will help you prepare for the midterm.

Leftovers from Wednesday#

Here is material I had prepared for Wednesday. I am equally happy to discuss whatever you think is most helpful to review.

Load the “taxis” dataset from Seaborn and drop rows with missing values.

import seaborn as sns
import altair as alt
import pandas as pd

df = sns.load_dataset("taxis")
df = df.dropna(axis=0)

Here is a reminder of how the dataset looks.

df.head()

	pickup	dropoff	passengers	distance	fare	tip	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough
0	2019-03-23 20:21:09	2019-03-23 20:27:24	1	1.60	7.0	2.15	12.95	yellow	credit card	Lenox Hill West	UN/Turtle Bay South	Manhattan	Manhattan
1	2019-03-04 16:11:55	2019-03-04 16:19:00	1	0.79	5.0	0.00	9.30	yellow	cash	Upper West Side South	Upper West Side South	Manhattan	Manhattan
2	2019-03-27 17:53:01	2019-03-27 18:00:25	1	1.37	7.5	2.36	14.16	yellow	credit card	Alphabet City	West Village	Manhattan	Manhattan
3	2019-03-10 01:23:59	2019-03-10 01:49:51	1	7.70	27.0	6.15	36.95	yellow	credit card	Hudson Sq	Yorkville West	Manhattan	Manhattan
4	2019-03-30 13:27:42	2019-03-30 13:37:14	3	2.16	9.0	1.10	13.40	yellow	credit card	Midtown East	Yorkville West	Manhattan	Manhattan

Multiply the values in columns from “distance” to “tolls” by 10

The following is a common mistake. With this syntax, pandas is looking for rows with the names “distance” to “tolls”, and since there aren’t any, the result is an “empty” DataFrame.

df.loc["distance":"tolls"]

	pickup	dropoff	passengers	distance	fare	tip	tolls	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough

Here is the “right” way to do this. Notice the : inside the square brackets, which says we want “every row”.

df.loc[:, "distance":"tolls"]*10

	distance	fare	tip	tolls
0	16.0	70.0	21.5	0.0
1	7.9	50.0	0.0	0.0
2	13.7	75.0	23.6	0.0
3	77.0	270.0	61.5	0.0
4	21.6	90.0	11.0	0.0
...	...	...	...	...
6428	7.5	45.0	10.6	0.0
6429	187.4	580.0	0.0	0.0
6430	41.4	160.0	0.0	0.0
6431	11.2	60.0	0.0	0.0
6432	38.5	150.0	33.6	0.0

6341 rows × 4 columns

Here is another way using a lambda function and applymap. This is definitely more confusing than simply doing *10 like we did above; I am just showing it as an example of using applymap to perform this basic task.

df.loc[:, "distance":"tolls"].applymap(lambda x: 10*x)

	distance	fare	tip	tolls
0	16.0	70.0	21.5	0.0
1	7.9	50.0	0.0	0.0
2	13.7	75.0	23.6	0.0
3	77.0	270.0	61.5	0.0
4	21.6	90.0	11.0	0.0
...	...	...	...	...
6428	7.5	45.0	10.6	0.0
6429	187.4	580.0	0.0	0.0
6430	41.4	160.0	0.0	0.0
6431	11.2	60.0	0.0	0.0
6432	38.5	150.0	33.6	0.0

6341 rows × 4 columns

The following doesn’t work because iloc slicing should be done using integers, not strings, so we need to get rid of the quotation marks.

df.iloc[:, "3":"7"]*10

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [9], line 1
----> 1 df.iloc[:, "3":"7"]*10

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:889, in _LocationIndexer.__getitem__(self, key)
    886         with suppress(KeyError, IndexError, AttributeError):
    887             # AttributeError for IntervalTree get_value
    888             return self.obj._get_value(*key, takeable=self._takeable)
--> 889     return self._getitem_tuple(key)
    890 else:
    891     # we by definition only have the 0th axis
    892     axis = self.axis or 0

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:1454, in _iLocIndexer._getitem_tuple(self, tup)
   1451 with suppress(IndexingError):
   1452     return self._getitem_lowerdim(tup)
-> 1454 return self._getitem_tuple_same_dim(tup)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:775, in _LocationIndexer._getitem_tuple_same_dim(self, tup)
    772 if com.is_null_slice(key):
    773     continue
--> 775 retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
    776 # We should never have retval.ndim < self.ndim, as that should
    777 #  be handled by the _getitem_lowerdim call above.
    778 assert retval.ndim == self.ndim

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:1481, in _iLocIndexer._getitem_axis(self, key, axis)
   1479 def _getitem_axis(self, key, axis: int):
   1480     if isinstance(key, slice):
-> 1481         return self._get_slice_axis(key, axis=axis)
   1483     if isinstance(key, list):
   1484         key = np.asarray(key)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:1513, in _iLocIndexer._get_slice_axis(self, slice_obj, axis)
   1510     return obj.copy(deep=False)
   1512 labels = obj._get_axis(axis)
-> 1513 labels._validate_positional_slice(slice_obj)
   1514 return self.obj._slice(slice_obj, axis=axis)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexes/base.py:3321, in Index._validate_positional_slice(self, key)
   3315 @final
   3316 def _validate_positional_slice(self, key: slice):
   3317     """
   3318     For positional indexing, a slice must have either int or None
   3319     for each of start, stop, and step.
   3320     """
-> 3321     self._validate_indexer("positional", key.start, "iloc")
   3322     self._validate_indexer("positional", key.stop, "iloc")
   3323     self._validate_indexer("positional", key.step, "iloc")

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexes/base.py:5310, in Index._validate_indexer(self, form, key, kind)
   5308     pass
   5309 else:
-> 5310     raise self._invalid_indexer(form, key)

TypeError: cannot do positional indexing on Index with these indexers [3] of type str

If you want to have staggered rows, not in a regular pattern, you need to write them all out explicitly in a list, like this.

df[["distance", "pickup", "pickup"]]

	distance	pickup	pickup
0	1.60	2019-03-23 20:21:09	2019-03-23 20:21:09
1	0.79	2019-03-04 16:11:55	2019-03-04 16:11:55
2	1.37	2019-03-27 17:53:01	2019-03-27 17:53:01
3	7.70	2019-03-10 01:23:59	2019-03-10 01:23:59
4	2.16	2019-03-30 13:27:42	2019-03-30 13:27:42
...	...	...	...
6428	0.75	2019-03-31 09:51:53	2019-03-31 09:51:53
6429	18.74	2019-03-31 17:38:00	2019-03-31 17:38:00
6430	4.14	2019-03-23 22:55:18	2019-03-23 22:55:18
6431	1.12	2019-03-04 10:09:25	2019-03-04 10:09:25
6432	3.85	2019-03-13 19:31:22	2019-03-13 19:31:22

6341 rows × 3 columns

Remember that map is used for a Series and applymap is used for a DataFrame. (Otherwise they are very similar.) Here is an example of the error we get if we try to use map on a DataFrame.

df.iloc[:, 3:7].map(lambda x: 10*x)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In [13], line 1
----> 1 df.iloc[:, 3:7].map(lambda x: 10*x)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/generic.py:5465, in NDFrame.__getattr__(self, name)
   5463 if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5464     return self[name]
-> 5465 return object.__getattribute__(self, name)

AttributeError: 'DataFrame' object has no attribute 'map'

Here is an example of a pandas Series. The prototypical example of a pandas Series is a column (or row) from a pandas DataFrame.

df["distance"]

      1.60
      0.79
      1.37
      7.70
      2.16
        ...  
   0.75
  18.74
   4.14
   1.12
   3.85
Name: distance, Length: 6341, dtype: float64

If we try to use applymap on a pandas Series, we get a similar error to what we got above.

df["distance"].applymap(lambda zzz: 10*zzz)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In [16], line 1
----> 1 df["distance"].applymap(lambda zzz: 10*zzz)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/generic.py:5465, in NDFrame.__getattr__(self, name)
   5463 if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5464     return self[name]
-> 5465 return object.__getattribute__(self, name)

AttributeError: 'Series' object has no attribute 'applymap'

If we use map on a pandas Series, on the other hand, it works. Notice that we can use any variable name we want in our lambda function; here we use zzz for our variable name.

df["distance"].map(lambda zzz: 10*zzz)

      16.0
       7.9
      13.7
      77.0
      21.6
        ...  
    7.5
  187.4
   41.4
   11.2
   38.5
Name: distance, Length: 6341, dtype: float64

Here is an example of slicing using iloc. We get the column at integer location 3 through the column at integer location 7 (exclusive).

df.iloc[:, 3:7]

	distance	fare	tip	tolls
0	1.60	7.0	2.15	0.0
1	0.79	5.0	0.00	0.0
2	1.37	7.5	2.36	0.0
3	7.70	27.0	6.15	0.0
4	2.16	9.0	1.10	0.0
...	...	...	...	...
6428	0.75	4.5	1.06	0.0
6429	18.74	58.0	0.00	0.0
6430	4.14	16.0	0.00	0.0
6431	1.12	6.0	0.00	0.0
6432	3.85	15.0	3.36	0.0

6341 rows × 4 columns

Here is some leftover code from Wednesday. I don’t plan to require list comprehension on the midterm, but I may ask you to make a list, and list comprehension is often the most convenient way to make a list.

day_list = [pd.to_datetime(f"4-{i}-2023").day_name() for i in range(24, 31)]
day_list

['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

Practice with `groupby` and f-strings#

Make a new column named “Day” in the DataFrame, containing the day of the pickup (as a string, like "Monday").

Here is a reminder of what df looks like.

df

	pickup	dropoff	passengers	distance	fare	tip	tolls	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough
0	2019-03-23 20:21:09	2019-03-23 20:27:24	1	1.60	7.0	2.15	0.0	12.95	yellow	credit card	Lenox Hill West	UN/Turtle Bay South	Manhattan	Manhattan
1	2019-03-04 16:11:55	2019-03-04 16:19:00	1	0.79	5.0	0.00	0.0	9.30	yellow	cash	Upper West Side South	Upper West Side South	Manhattan	Manhattan
2	2019-03-27 17:53:01	2019-03-27 18:00:25	1	1.37	7.5	2.36	0.0	14.16	yellow	credit card	Alphabet City	West Village	Manhattan	Manhattan
3	2019-03-10 01:23:59	2019-03-10 01:49:51	1	7.70	27.0	6.15	0.0	36.95	yellow	credit card	Hudson Sq	Yorkville West	Manhattan	Manhattan
4	2019-03-30 13:27:42	2019-03-30 13:37:14	3	2.16	9.0	1.10	0.0	13.40	yellow	credit card	Midtown East	Yorkville West	Manhattan	Manhattan
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6428	2019-03-31 09:51:53	2019-03-31 09:55:27	1	0.75	4.5	1.06	0.0	6.36	green	credit card	East Harlem North	Central Harlem North	Manhattan	Manhattan
6429	2019-03-31 17:38:00	2019-03-31 18:34:23	1	18.74	58.0	0.00	0.0	58.80	green	credit card	Jamaica	East Concourse/Concourse Village	Queens	Bronx
6430	2019-03-23 22:55:18	2019-03-23 23:14:25	1	4.14	16.0	0.00	0.0	17.30	green	cash	Crown Heights North	Bushwick North	Brooklyn	Brooklyn
6431	2019-03-04 10:09:25	2019-03-04 10:14:29	1	1.12	6.0	0.00	0.0	6.80	green	credit card	East New York	East Flatbush/Remsen Village	Brooklyn	Brooklyn
6432	2019-03-13 19:31:22	2019-03-13 19:48:02	1	3.85	15.0	3.36	0.0	20.16	green	credit card	Boerum Hill	Windsor Terrace	Brooklyn	Brooklyn

6341 rows × 14 columns

Here we add a new column to the far right side of the DataFrame. Notice that, unlike usual, we did not need to use pd.to_datetime.

df["Day"] = df["pickup"].dt.day_name()
df

	pickup	dropoff	passengers	distance	fare	tip	tolls	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough	Day
0	2019-03-23 20:21:09	2019-03-23 20:27:24	1	1.60	7.0	2.15	0.0	12.95	yellow	credit card	Lenox Hill West	UN/Turtle Bay South	Manhattan	Manhattan	Saturday
1	2019-03-04 16:11:55	2019-03-04 16:19:00	1	0.79	5.0	0.00	0.0	9.30	yellow	cash	Upper West Side South	Upper West Side South	Manhattan	Manhattan	Monday
2	2019-03-27 17:53:01	2019-03-27 18:00:25	1	1.37	7.5	2.36	0.0	14.16	yellow	credit card	Alphabet City	West Village	Manhattan	Manhattan	Wednesday
3	2019-03-10 01:23:59	2019-03-10 01:49:51	1	7.70	27.0	6.15	0.0	36.95	yellow	credit card	Hudson Sq	Yorkville West	Manhattan	Manhattan	Sunday
4	2019-03-30 13:27:42	2019-03-30 13:37:14	3	2.16	9.0	1.10	0.0	13.40	yellow	credit card	Midtown East	Yorkville West	Manhattan	Manhattan	Saturday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6428	2019-03-31 09:51:53	2019-03-31 09:55:27	1	0.75	4.5	1.06	0.0	6.36	green	credit card	East Harlem North	Central Harlem North	Manhattan	Manhattan	Sunday
6429	2019-03-31 17:38:00	2019-03-31 18:34:23	1	18.74	58.0	0.00	0.0	58.80	green	credit card	Jamaica	East Concourse/Concourse Village	Queens	Bronx	Sunday
6430	2019-03-23 22:55:18	2019-03-23 23:14:25	1	4.14	16.0	0.00	0.0	17.30	green	cash	Crown Heights North	Bushwick North	Brooklyn	Brooklyn	Saturday
6431	2019-03-04 10:09:25	2019-03-04 10:14:29	1	1.12	6.0	0.00	0.0	6.80	green	credit card	East New York	East Flatbush/Remsen Village	Brooklyn	Brooklyn	Monday
6432	2019-03-13 19:31:22	2019-03-13 19:48:02	1	3.85	15.0	3.36	0.0	20.16	green	credit card	Boerum Hill	Windsor Terrace	Brooklyn	Brooklyn	Wednesday

6341 rows × 15 columns

Why didn’t we have to use pd.to_datetime? Evaluate df.dtypes and df.dtypes["pickup"] to see the reason.

The reason is that when we imported the DataFrame from Seaborn (as opposed to from an attached csv file), Seaborn automatically specified the appropriate data types for these columns.

df.dtypes

pickup             datetime64[ns]
dropoff            datetime64[ns]
passengers                  int64
distance                  float64
fare                      float64
tip                       float64
tolls                     float64
total                     float64
color                      object
payment                    object
pickup_zone                object
dropoff_zone               object
pickup_borough             object
dropoff_borough            object
Day                        object
dtype: object

The above result, df.dtypes, is another example of a pandas Series (like a column). Here is an example of getting the value corresponding to the index entry “pickup”. (The value is displayed a little strangely, but it’s indicating that this column has a datetime data type.)

df.dtypes["pickup"]

dtype('<M8[ns]')

If we look at the value corresponding to “distance”, it’s easier to read, because we recognize the term float for this numeric column.

df.dtypes["distance"]

dtype('float64')

What day occurs most often in this dataset?

Here is a nice way to find that. The value_counts method automatically sorts the results from most common to least common. The output of value_counts() is another example of a pandas Series. The index in this case is the unique entries occurring, and the values are how often they occur.

vc = df["Day"].value_counts()
vc

Friday       1091
Saturday     1028
Wednesday     958
Thursday      896
Sunday        857
Tuesday       811
Monday        700
Name: Day, dtype: int64

Here we check the index explicitly, using the index attribute.

vc.index

Index(['Friday', 'Saturday', 'Wednesday', 'Thursday', 'Sunday', 'Tuesday',
       'Monday'],
      dtype='object')

If instead we want the values, we can use the values attribute.

vc.values

array([1091, 1028,  958,  896,  857,  811,  700])

If you don’t care about the frequency of the entries, you can instead use the unique method.

df["Day"].unique()

array(['Saturday', 'Monday', 'Wednesday', 'Sunday', 'Tuesday', 'Friday',
       'Thursday'], dtype=object)

If we want to know the most frequent entry, we can get the initial element in the value_counts index.

vc.index[0]

'Friday'

Can you answer the same thing using Altair?

By default, Altair does not allow us to use DataFrames with more than 5000 rows. This is an example of the error message you’ll see if you try.

alt.Chart(df).mark_bar().encode(
    x="Day",
    y="count()"
)

---------------------------------------------------------------------------
MaxRowsError                              Traceback (most recent call last)
File /shared-libs/python3.9/py/lib/python3.9/site-packages/altair/vegalite/v4/api.py:2020, in Chart.to_dict(self, *args, **kwargs)
   2018     copy.data = core.InlineData(values=[{}])
   2019     return super(Chart, copy).to_dict(*args, **kwargs)
-> 2020 return super().to_dict(*args, **kwargs)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/altair/vegalite/v4/api.py:374, in TopLevelMixin.to_dict(self, *args, **kwargs)
    372 copy = self.copy(deep=False)
    373 original_data = getattr(copy, "data", Undefined)
--> 374 copy.data = _prepare_data(original_data, context)
    376 if original_data is not Undefined:
    377     context["data"] = original_data

File /shared-libs/python3.9/py/lib/python3.9/site-packages/altair/vegalite/v4/api.py:89, in _prepare_data(data, context)
     87 # convert dataframes  or objects with __geo_interface__ to dict
     88 if isinstance(data, pd.DataFrame) or hasattr(data, "__geo_interface__"):
---> 89     data = _pipe(data, data_transformers.get())
     91 # convert string input to a URLData
     92 if isinstance(data, str):

File /shared-libs/python3.9/py/lib/python3.9/site-packages/toolz/functoolz.py:628, in pipe(data, *funcs)
    608 """ Pipe a value through a sequence of functions
    609 
    610 I.e. ``pipe(data, f, g, h)`` is equivalent to ``h(g(f(data)))``
   (...)
    625     thread_last
    626 """
    627 for func in funcs:
--> 628     data = func(data)
    629 return data

File /shared-libs/python3.9/py/lib/python3.9/site-packages/toolz/functoolz.py:304, in curry.__call__(self, *args, **kwargs)
    302 def __call__(self, *args, **kwargs):
    303     try:
--> 304         return self._partial(*args, **kwargs)
    305     except TypeError as exc:
    306         if self._should_curry(args, kwargs, exc):

File /shared-libs/python3.9/py/lib/python3.9/site-packages/altair/vegalite/data.py:19, in default_data_transformer(data, max_rows)
     17 @curried.curry
     18 def default_data_transformer(data, max_rows=5000):
---> 19     return curried.pipe(data, limit_rows(max_rows=max_rows), to_values)

File /shared-libs/python3.9/py/lib/python3.9/site-packages/toolz/functoolz.py:628, in pipe(data, *funcs)
    608 """ Pipe a value through a sequence of functions
    609 
    610 I.e. ``pipe(data, f, g, h)`` is equivalent to ``h(g(f(data)))``
   (...)
    625     thread_last
    626 """
    627 for func in funcs:
--> 628     data = func(data)
    629 return data

File /shared-libs/python3.9/py/lib/python3.9/site-packages/toolz/functoolz.py:304, in curry.__call__(self, *args, **kwargs)
    302 def __call__(self, *args, **kwargs):
    303     try:
--> 304         return self._partial(*args, **kwargs)
    305     except TypeError as exc:
    306         if self._should_curry(args, kwargs, exc):

File /shared-libs/python3.9/py/lib/python3.9/site-packages/altair/utils/data.py:80, in limit_rows(data, max_rows)
     78         return data
     79 if max_rows is not None and len(values) > max_rows:
---> 80     raise MaxRowsError(
     81         "The number of rows in your dataset is greater "
     82         "than the maximum allowed ({}). "
     83         "For information on how to plot larger datasets "
     84         "in Altair, see the documentation".format(max_rows)
     85     )
     86 return data

MaxRowsError: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation

alt.Chart(...)

Here we tell Altair to allow up to 7,000 rows. Warning: Don’t try to do this with huge datasets. I wouldn’t try it with more than about 20,000 rows at most.

Don’t worry about learning this data_transformers syntax for the midterms or quizzes.

# Our dataset has about 6500 rows.  Be careful allowing too many rows.
alt.data_transformers.enable('default', max_rows=7000)

DataTransformerRegistry.enable('default')

Here we put the days of the week along the x-axis, and we use the special Altair syntax "count()" to make the heights of the bars equal to the number of entries for that day. Notice how, like we saw above, Friday seems to be the most common.

It’s a little annoying that the days are alphabetized, rather than given in consecutive order, but let’s not worry about trying to fix that for now (it’s definitely possible to fix it).

alt.Chart(df).mark_bar().encode(
    x="Day",
    y="count()"
)

What happens if we don’t specify the y-channel? Then the bars are all made with a default height.

alt.Chart(df).mark_bar().encode(
    x="Day",
)

Instead of "count()", we could use another function like "median", but in that case, we need to specify what is the value we are taking the median of. Here we take the median of the “tip” column.

Using the tooltip, we can see that the median value is 1.76 for the “tip” column on Tuesdays.

alt.Chart(df).mark_bar().encode(
    x="Day",
    y="median(tip)",
    tooltip=["median(tip)"]
)

What is the median tip amount for Tuesday?

Here we get the sub-DataFrame.

df_sub = df[df["Day"] == "Tuesday"]

Notice how the values seem to all be on Tuesday.

df_sub.head()

	pickup	dropoff	passengers	distance	fare	tip	tolls	total	color	payment	pickup_zone	dropoff_zone	pickup_borough	dropoff_borough	Day
6	2019-03-26 21:07:31	2019-03-26 21:17:29	1	3.65	13.0	2.00	0.00	18.80	yellow	credit card	Battery Park City	Two Bridges/Seward Park	Manhattan	Manhattan	Tuesday
13	2019-03-19 07:55:25	2019-03-19 08:09:17	1	1.75	10.5	0.00	0.00	13.80	yellow	cash	Lincoln Square West	Times Sq/Theatre District	Manhattan	Manhattan	Tuesday
56	2019-03-05 17:57:00	2019-03-05 18:45:25	2	19.59	52.0	0.00	5.76	65.56	yellow	credit card	JFK Airport	Upper East Side North	Queens	Manhattan	Tuesday
64	2019-03-26 10:14:55	2019-03-26 10:26:58	6	1.38	9.0	3.08	0.00	15.38	yellow	credit card	Midtown South	Midtown Center	Manhattan	Manhattan	Tuesday
72	2019-03-12 15:44:53	2019-03-12 16:02:05	1	1.98	11.5	2.96	0.00	17.76	yellow	credit card	Yorkville West	Upper West Side North	Manhattan	Manhattan	Tuesday

Here we compute the median of the tip column. (Notice how above we only saw 5 rows, but that is because we called the head() method. In the following cell, we aren’t calling the head() method, so our DataFrame has many more rows.)

We get the same value here, but it’s also possible for there to be different conventions between pandas and Altair. For example, maybe Altair would count missing values as 0 (just hypothetically) and maybe pandas would ignore them. But in this case, we do get the same output.

df_sub["tip"].median()

1.76

For each day of the week, what was the median tip for that day? Put the results into a pandas Series using groupby.

Can you answer the same thing using Altair?

For each day of the week, print out a string stating the median tip amount for that day. Use :.2f in the string formatting part so that only two decimal places get printed.

Week 4 Friday

Contents

Week 4 Friday#

Announcements#

Leftovers from Wednesday#

Practice with groupby and f-strings#

Practice with `groupby` and f-strings#