Groupby and Indexing¶

In this lesson, we'll learn about an important DataFrame operation called groupby. Along the way, we'll also discuss how this groupby operation introduces an extra level of complexity toward indexing and slicing values. By the end of this lesson, students will be able to:

  • Apply the groupby operation to a list of dictionaries and to a pandas DataFrame.
  • Select values from a hierarchical index using tuples and the slice object as keys.
  • Apply the apply operation to a list of dictionaries and to a pandas DataFrame.

Previously, we learned how to find the largest earthquake in a dataset using both a list of dictionaries and using a pandas DataFrame. How about finding the largest earthquake for each place in the dataset?

In [1]:
import doctest
import io
import pandas as pd

To help visualize our work, the following dataset contains the first 12 rows from earthquakes.csv.

In [2]:
csv = """
id,year,month,day,latitude,longitude,name,magnitude
nc72666881,2016,7,27,37.6723333,-121.619,California,1.43
us20006i0y,2016,7,27,21.5146,94.5721,Burma,4.9
nc72666891,2016,7,27,37.5765,-118.85916670000002,California,0.06
nc72666896,2016,7,27,37.5958333,-118.99483329999998,California,0.4
nn00553447,2016,7,27,39.3775,-119.845,Nevada,0.3
ak13805337,2016,7,27,61.2963,-152.46,Alaska,1.8
hv61354276,2016,7,27,19.4235,-155.60983330000005,Hawaii,1.0
ak13805339,2016,7,27,61.3019,-152.4507,Alaska,2.0
ci37640584,2016,7,27,35.503,-118.40583329999998,California,1.2
nc72666901,2016,7,27,37.673,-121.6133333,California,1.67
ci37640592,2016,7,27,33.5888333,-116.8165,California,0.48
nn00553416,2016,7,27,38.2638,-118.7351,Nevada,0.9
"""

earthquakes = pd.read_csv(io.StringIO(csv), index_col="id")
earthquakes
Out[2]:
year month day latitude longitude name magnitude
id
nc72666881 2016 7 27 37.672333 -121.619000 California 1.43
us20006i0y 2016 7 27 21.514600 94.572100 Burma 4.90
nc72666891 2016 7 27 37.576500 -118.859167 California 0.06
nc72666896 2016 7 27 37.595833 -118.994833 California 0.40
nn00553447 2016 7 27 39.377500 -119.845000 Nevada 0.30
ak13805337 2016 7 27 61.296300 -152.460000 Alaska 1.80
hv61354276 2016 7 27 19.423500 -155.609833 Hawaii 1.00
ak13805339 2016 7 27 61.301900 -152.450700 Alaska 2.00
ci37640584 2016 7 27 35.503000 -118.405833 California 1.20
nc72666901 2016 7 27 37.673000 -121.613333 California 1.67
ci37640592 2016 7 27 33.588833 -116.816500 California 0.48
nn00553416 2016 7 27 38.263800 -118.735100 Nevada 0.90

Groupby in plain Python¶

Let's first see how we can solve this problem using the list of dictionaries approach.

In [3]:
max_mag_by_place = {}
for earthquake in earthquakes.to_dict("records"): # Convert to list of dictionaries
    if earthquake["name"] not in max_mag_by_place or earthquake["magnitude"] > max_mag_by_place[earthquake["name"]]:
        max_mag_by_place[earthquake["name"]] = earthquake["magnitude"]

max_mag_by_place
Out[3]:
{'California': 1.67, 'Burma': 4.9, 'Nevada': 0.9, 'Alaska': 2.0, 'Hawaii': 1.0}

Groupby in Pandas¶

The inventors of pandas defined a DataFrame function called groupby to streamline this operation into a single expression.

In [4]:
earthquakes.groupby("name")["magnitude"].max()
Out[4]:
name
Alaska        2.00
Burma         4.90
California    1.67
Hawaii        1.00
Nevada        0.90
Name: magnitude, dtype: float64

What's going on here? We can take a closer view at each step of the process in PandasTutor. In summary, this expression:

  1. Calls earthquakes.groupby("name") to split the earthquakes into groups by "name".
  2. For each group, selects the column "magnitude" indicated in square brackets.
  3. Combines (summarizes) each group on the selected column using the max() function.

groupby help us quickly answer questions involving "grouping by" one or more columns and then summarizing data in another column.

The best part about pandas groupby is that it allows us to quickly answer many different kinds of questions following the same format. For example, suppose we want to compute descriptive statistics for all the earthquake magnitudes that occurred on each day. Let's read the full dataset and try it out.

In [5]:
earthquakes = pd.read_csv("earthquakes.csv", index_col="id")
earthquakes
Out[5]:
year month day latitude longitude name magnitude
id
nc72666881 2016 7 27 37.672333 -121.619000 California 1.43
us20006i0y 2016 7 27 21.514600 94.572100 Burma 4.90
nc72666891 2016 7 27 37.576500 -118.859167 California 0.06
nc72666896 2016 7 27 37.595833 -118.994833 California 0.40
nn00553447 2016 7 27 39.377500 -119.845000 Nevada 0.30
... ... ... ... ... ... ... ...
nc72685246 2016 8 25 36.515499 -121.099831 California 2.42
ak13879193 2016 8 25 61.498400 -149.862700 Alaska 1.40
nc72685251 2016 8 25 38.805000 -122.821503 California 1.06
ci37672328 2016 8 25 34.308000 -118.635333 California 1.55
ci37672360 2016 8 25 34.119167 -116.933667 California 0.89

8394 rows × 7 columns

In [6]:
magnitudes_per_day = earthquakes.groupby(["year", "month", "day"])["magnitude"].describe()
magnitudes_per_day
Out[6]:
count mean std min 25% 50% 75% max
year month day
2016 7 27 272.0 1.617574 1.095349 0.06 0.9000 1.400 2.0000 5.60
28 308.0 1.448149 0.896851 0.10 0.8775 1.240 1.8000 5.10
29 309.0 1.640129 1.165952 0.01 0.8800 1.450 1.9000 7.70
30 329.0 1.615076 1.262618 0.03 0.7000 1.240 2.0000 5.70
31 278.0 1.750827 1.261577 0.10 0.9000 1.500 2.1475 5.90
8 1 356.0 1.520056 1.157326 0.04 0.8000 1.245 1.8025 6.10
2 292.0 1.539418 1.089946 0.05 0.8000 1.300 1.9000 5.50
3 294.0 1.556327 1.147365 0.01 0.8300 1.200 1.8150 5.10
4 420.0 1.249190 1.034738 0.05 0.6000 1.000 1.5825 6.30
5 256.0 1.428789 1.144244 0.10 0.6200 1.185 1.7150 5.70
6 316.0 1.313228 1.065587 0.09 0.5600 1.100 1.6200 5.40
7 316.0 1.356994 1.078556 0.10 0.6000 1.120 1.7425 5.10
8 335.0 1.484925 1.131495 0.02 0.6300 1.200 1.9900 5.10
9 272.0 1.614779 1.164186 0.10 0.8075 1.300 1.9000 5.09
10 329.0 1.404742 1.038701 0.02 0.7700 1.170 1.7400 5.40
11 356.0 1.390534 1.159147 0.04 0.6775 1.100 1.7000 7.20
12 326.0 1.533282 1.158696 0.04 0.7400 1.200 1.9000 5.40
13 284.0 1.421901 1.080338 0.10 0.7000 1.105 1.7775 5.20
14 231.0 1.692684 1.372191 0.05 0.8200 1.200 1.9000 5.90
15 222.0 1.583964 1.157553 0.07 0.8550 1.300 1.8400 5.40
16 223.0 1.629910 1.223131 0.10 0.7300 1.300 2.0000 5.10
17 220.0 1.583682 1.203617 0.10 0.8000 1.210 1.9675 5.20
18 219.0 1.499772 1.159497 0.04 0.7600 1.200 1.8050 5.90
19 226.0 1.819469 1.416291 0.11 0.8925 1.340 2.1900 7.40
20 237.0 1.553207 1.296262 0.02 0.7000 1.200 2.0000 6.40
21 266.0 1.332368 1.032210 0.03 0.6050 1.140 1.6925 5.10
22 215.0 1.451488 1.185657 0.01 0.7000 1.180 1.8000 5.60
23 233.0 1.643391 1.245661 0.04 0.8200 1.300 2.1000 6.20
24 216.0 1.553194 1.144054 0.03 0.8375 1.290 1.9000 6.80
25 238.0 1.519328 0.926028 0.10 0.8900 1.400 1.9950 5.90

Explain in your own words the result of the following code snippet.

In [7]:
# the maximum latitude of the earthquakes that happened in the "place" 
earthquakes.groupby("name")["latitude"].max()
Out[7]:
name
Afghanistan                       36.634500
Alaska                            70.778700
Anguilla                          18.435800
Argentina                        -22.394200
Arizona                           36.811667
                                    ...    
Washington                        48.965667
West Virginia                     37.863000
Western Indian-Antarctic Ridge   -49.281000
Western Xizang                    34.444600
Wyoming                           44.749000
Name: latitude, Length: 118, dtype: float64

Hierarchical indexing¶

If you look closely at the magnitudes_per_day DataFrame, you'll notice something interesting: there are three index columns in bold on the left to denote each year, month, and day group. In pandas, a DataFrame can have a hierarchical (aka multi-level) index called a MultiIndex.

In [8]:
magnitudes_per_day.index
Out[8]:
MultiIndex([(2016, 7, 27),
            (2016, 7, 28),
            (2016, 7, 29),
            (2016, 7, 30),
            (2016, 7, 31),
            (2016, 8,  1),
            (2016, 8,  2),
            (2016, 8,  3),
            (2016, 8,  4),
            (2016, 8,  5),
            (2016, 8,  6),
            (2016, 8,  7),
            (2016, 8,  8),
            (2016, 8,  9),
            (2016, 8, 10),
            (2016, 8, 11),
            (2016, 8, 12),
            (2016, 8, 13),
            (2016, 8, 14),
            (2016, 8, 15),
            (2016, 8, 16),
            (2016, 8, 17),
            (2016, 8, 18),
            (2016, 8, 19),
            (2016, 8, 20),
            (2016, 8, 21),
            (2016, 8, 22),
            (2016, 8, 23),
            (2016, 8, 24),
            (2016, 8, 25)],
           names=['year', 'month', 'day'])

A MultiIndex is .loc-accessible with Python tuples. However, the syntax is somewhat unusual, particularly when combined with slicing due to limitations in the Python programming language. For each example below, predict the output type (single value, 1-d Series, or 2-d DataFrame) as well as the contents of the output before running it.

In [9]:
magnitudes_per_day
Out[9]:
count mean std min 25% 50% 75% max
year month day
2016 7 27 272.0 1.617574 1.095349 0.06 0.9000 1.400 2.0000 5.60
28 308.0 1.448149 0.896851 0.10 0.8775 1.240 1.8000 5.10
29 309.0 1.640129 1.165952 0.01 0.8800 1.450 1.9000 7.70
30 329.0 1.615076 1.262618 0.03 0.7000 1.240 2.0000 5.70
31 278.0 1.750827 1.261577 0.10 0.9000 1.500 2.1475 5.90
8 1 356.0 1.520056 1.157326 0.04 0.8000 1.245 1.8025 6.10
2 292.0 1.539418 1.089946 0.05 0.8000 1.300 1.9000 5.50
3 294.0 1.556327 1.147365 0.01 0.8300 1.200 1.8150 5.10
4 420.0 1.249190 1.034738 0.05 0.6000 1.000 1.5825 6.30
5 256.0 1.428789 1.144244 0.10 0.6200 1.185 1.7150 5.70
6 316.0 1.313228 1.065587 0.09 0.5600 1.100 1.6200 5.40
7 316.0 1.356994 1.078556 0.10 0.6000 1.120 1.7425 5.10
8 335.0 1.484925 1.131495 0.02 0.6300 1.200 1.9900 5.10
9 272.0 1.614779 1.164186 0.10 0.8075 1.300 1.9000 5.09
10 329.0 1.404742 1.038701 0.02 0.7700 1.170 1.7400 5.40
11 356.0 1.390534 1.159147 0.04 0.6775 1.100 1.7000 7.20
12 326.0 1.533282 1.158696 0.04 0.7400 1.200 1.9000 5.40
13 284.0 1.421901 1.080338 0.10 0.7000 1.105 1.7775 5.20
14 231.0 1.692684 1.372191 0.05 0.8200 1.200 1.9000 5.90
15 222.0 1.583964 1.157553 0.07 0.8550 1.300 1.8400 5.40
16 223.0 1.629910 1.223131 0.10 0.7300 1.300 2.0000 5.10
17 220.0 1.583682 1.203617 0.10 0.8000 1.210 1.9675 5.20
18 219.0 1.499772 1.159497 0.04 0.7600 1.200 1.8050 5.90
19 226.0 1.819469 1.416291 0.11 0.8925 1.340 2.1900 7.40
20 237.0 1.553207 1.296262 0.02 0.7000 1.200 2.0000 6.40
21 266.0 1.332368 1.032210 0.03 0.6050 1.140 1.6925 5.10
22 215.0 1.451488 1.185657 0.01 0.7000 1.180 1.8000 5.60
23 233.0 1.643391 1.245661 0.04 0.8200 1.300 2.1000 6.20
24 216.0 1.553194 1.144054 0.03 0.8375 1.290 1.9000 6.80
25 238.0 1.519328 0.926028 0.10 0.8900 1.400 1.9950 5.90
In [10]:
magnitudes_per_day.loc[(2016, 7, 27), "count"]
Out[10]:
272.0
In [11]:
magnitudes_per_day.loc[:, "count"]
Out[11]:
year  month  day
2016  7      27     272.0
             28     308.0
             29     309.0
             30     329.0
             31     278.0
      8      1      356.0
             2      292.0
             3      294.0
             4      420.0
             5      256.0
             6      316.0
             7      316.0
             8      335.0
             9      272.0
             10     329.0
             11     356.0
             12     326.0
             13     284.0
             14     231.0
             15     222.0
             16     223.0
             17     220.0
             18     219.0
             19     226.0
             20     237.0
             21     266.0
             22     215.0
             23     233.0
             24     216.0
             25     238.0
Name: count, dtype: float64
In [12]:
magnitudes_per_day.loc[(2016, 8, 10:15), "count"]

  Cell In[12], line 1
    magnitudes_per_day.loc[(2016, 8, 10:15), "count"]
                                       ^
SyntaxError: invalid syntax
In [15]:
slice(10, 15)
Out[15]:
slice(10, 15, None)
In [16]:
magnitudes_per_day.loc[(2016, 8, slice(10,15)), "count"]
Out[16]:
year  month  day
2016  8      10     329.0
             11     356.0
             12     326.0
             13     284.0
             14     231.0
             15     222.0
Name: count, dtype: float64
In [17]:
magnitudes_per_day.loc[[(2016, 8, 10), (2016, 8, 15)], "count"]
Out[17]:
year  month  day
2016  8      10     329.0
             15     222.0
Name: count, dtype: float64
In [18]:
magnitudes_per_day.loc[magnitudes_per_day["count"] < 220, "count"]
Out[18]:
year  month  day
2016  8      18     219.0
             22     215.0
             24     216.0
Name: count, dtype: float64

Poll question: what if we want to get all dates after 2016-08-10?

In [19]:
"magnitude"[2:]
Out[19]:
'gnitude'
In [20]:
magnitudes_per_day.loc[(2016, 8, slice(10, None)), "count"]
Out[20]:
year  month  day
2016  8      10     329.0
             11     356.0
             12     326.0
             13     284.0
             14     231.0
             15     222.0
             16     223.0
             17     220.0
             18     219.0
             19     226.0
             20     237.0
             21     266.0
             22     215.0
             23     233.0
             24     216.0
             25     238.0
Name: count, dtype: float64
In [21]:
magnitudes_per_day.loc[(2016, 8, slice(10)), "count"]
Out[21]:
year  month  day
2016  8      1      356.0
             2      292.0
             3      294.0
             4      420.0
             5      256.0
             6      316.0
             7      316.0
             8      335.0
             9      272.0
             10     329.0
Name: count, dtype: float64
In [ ]:
magnitudes_per_day.loc[2016, 8, 10:, "count"]
In [23]:
magnitudes_per_day.loc[(2016, 8, 10:), "count"]

  Cell In[23], line 1
    magnitudes_per_day.loc[(2016, 8, 10:), "count"]
                                       ^
SyntaxError: invalid syntax

Practice: UFO sightings¶

UFO (unidentified flying object) sightings have received attention from US Congress in the past couple years. We've collected a public dataset consisting of 1001 reported UFO sightings around the world to help us practice groupby operations.

In [24]:
ufos = pd.read_csv("ufos.csv", index_col="datetime")
ufos
Out[24]:
city state country shape duration (seconds) duration (hours/min) comments date posted latitude longitude
datetime
10/10/1949 20:30 san marcos tx us cylinder 2700.0 45 minutes This event took place in early fall around 194... 4/27/2004 29.883056 -97.941111
10/10/1949 21:00 lackland afb tx NaN light 7200.0 1-2 hrs 1949 Lackland AFB&#44 TX. Lights racing acros... 12/16/2005 29.384210 -98.581082
10/10/1955 17:00 chester (uk/england) NaN gb circle 20.0 20 seconds Green/Orange circular disc over Chester&#44 En... 1/21/2008 53.200000 -2.916667
10/10/1956 21:00 edna tx us circle 20.0 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.978333 -96.645833
10/10/1960 20:00 kaneohe hi us light 900.0 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 -157.803611
... ... ... ... ... ... ... ... ... ... ...
10/12/1982 05:30 pearlington ms us light 29.0 29 seconds Round light which was observed going into spac... 12/12/2013 30.246389 -89.611111
10/12/1985 23:45 swansboro nc us disk 300.0 23:45 - 23:50 My sister and I observed a disk for about 5 mi... 12/2/2000 34.687500 -77.119444
10/12/1988 16:30 melbourne (vic&#44 australia) NaN au cigar 900.0 15 min&#39s Large cigar shaped craft&#44flying sideways&#4... 9/29/2004 -37.813938 144.963425
10/12/1994 11:55 schenectady ny us other 120.0 a few minutes I had just picked up my Daugther (2yrs old) fr... 4/1/2001 42.814167 -73.940000
10/12/1994 15:00 monticello ky us chevron 120.0 1-2 minutes Triangular/chevron small object with fixed lig... 10/30/2006 36.829722 -84.849167

1001 rows × 10 columns

Compute the average (mean) "duration (seconds)" for each UFO "shape".

In [25]:
ufos.groupby("shape")["duration (seconds)"].mean()
Out[25]:
shape
changing     9.265600e+02
chevron      3.111250e+02
cigar        8.217407e+02
circle       6.804353e+02
cone         3.000000e+02
cross        6.000000e+01
cylinder     1.499556e+03
delta        1.440000e+04
diamond      1.140300e+03
disk         1.143329e+03
egg          3.088000e+03
fireball     3.467656e+02
flash        4.639091e+02
formation    1.913088e+03
light        1.122005e+03
other        9.259301e+04
oval         1.425136e+03
rectangle    2.285882e+02
sphere       1.506268e+06
teardrop     1.397143e+02
triangle     7.352900e+02
unknown      1.207963e+03
Name: duration (seconds), dtype: float64

Since we're focusing on US Congress, identify the UFO sighting with the longest "duration (seconds)" for each "city" in the US ("us"). Do not include any cities outside the US.

In [28]:
ufos[ufos["country"] == "us"].groupby("city")["duration (seconds)"].max()
Out[28]:
city
acton                                   180.0
addison (i-355 and us 20 (lake st.)     600.0
albany                                  120.0
albuquerque                            3600.0
algona                                 3600.0
                                        ...  
wolfforth                               300.0
worcester                                 4.0
yakima                                  240.0
york                                     15.0
yuma                                    900.0
Name: duration (seconds), Length: 627, dtype: float64
In [38]:
ufos.loc[ufos["country"] == "us"]
Out[38]:
city state country shape duration (seconds) duration (hours/min) comments date posted latitude longitude word count
datetime
10/10/1949 20:30 san marcos tx us cylinder 2700.0 45 minutes This event took place in early fall around 194... 4/27/2004 29.883056 -97.941111 24
10/10/1956 21:00 edna tx us circle 20.0 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.978333 -96.645833 26
10/10/1960 20:00 kaneohe hi us light 900.0 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 -157.803611 25
10/10/1961 19:00 bristol tn us sphere 300.0 5 minutes My father is now 89 my brother 52 the girl wit... 4/27/2007 36.595000 -82.188889 29
10/10/1965 23:45 norwalk ct us disk 1200.0 20 minutes A bright orange color changing to reddish colo... 10/2/1999 41.117500 -73.408333 16
... ... ... ... ... ... ... ... ... ... ... ...
10/12/1982 05:30 pearlington ms us light 20.0 20 seconds While fishing on the east pearl river which se... 12/12/2013 30.246389 -89.611111 25
10/12/1982 05:30 pearlington ms us light 29.0 29 seconds Round light which was observed going into spac... 12/12/2013 30.246389 -89.611111 18
10/12/1985 23:45 swansboro nc us disk 300.0 23:45 - 23:50 My sister and I observed a disk for about 5 mi... 12/2/2000 34.687500 -77.119444 16
10/12/1994 11:55 schenectady ny us other 120.0 a few minutes I had just picked up my Daugther (2yrs old) fr... 4/1/2001 42.814167 -73.940000 28
10/12/1994 15:00 monticello ky us chevron 120.0 1-2 minutes Triangular/chevron small object with fixed lig... 10/30/2006 36.829722 -84.849167 10

823 rows × 11 columns

What is the name of the "city" that has the largest count of UFO sightings?

In [32]:
ufos.groupby("city")["city"].count().idxmax()
Out[32]:
'seattle'

String accessor functions¶

In data science, many tasks involve string data. In plain Python, we know that we can call string functions like split() to split a string on whitespace or find(target) to find the index that a target appears in a string.

To help improve readability of code, the inventors of pandas provide these functions as element-wise operations but hide them behind a special .str string accessor such as s.str.split().

In [33]:
ufos["comments"].str.split()
Out[33]:
datetime
10/10/1949 20:30    [This, event, took, place, in, early, fall, ar...
10/10/1949 21:00    [1949, Lackland, AFB&#44, TX., Lights, racing,...
10/10/1955 17:00    [Green/Orange, circular, disc, over, Chester&#...
10/10/1956 21:00    [My, older, brother, and, twin, sister, were, ...
10/10/1960 20:00    [AS, a, Marine, 1st, Lt., flying, an, FJ4B, fi...
                                          ...                        
10/12/1982 05:30    [Round, light, which, was, observed, going, in...
10/12/1985 23:45    [My, sister, and, I, observed, a, disk, for, a...
10/12/1988 16:30    [Large, cigar, shaped, craft&#44flying, sidewa...
10/12/1994 11:55    [I, had, just, picked, up, my, Daugther, (2yrs...
10/12/1994 15:00    [Triangular/chevron, small, object, with, fixe...
Name: comments, Length: 1001, dtype: object

The above expression splits each comment by whitespace. This isn't too useful on its own, but we can then compute the length of each list to find the number of words in each comment.

In [34]:
ufos["comments"].str.split().str.len()
Out[34]:
datetime
10/10/1949 20:30    24
10/10/1949 21:00    17
10/10/1955 17:00     6
10/10/1956 21:00    26
10/10/1960 20:00    25
                    ..
10/12/1982 05:30    18
10/12/1985 23:45    16
10/12/1988 16:30     8
10/12/1994 11:55    28
10/12/1994 15:00    10
Name: comments, Length: 1001, dtype: int64

These functions don't modify the original DataFrame. To add the result as a new column in the original DataFrame, use an assignment statement.

In [35]:
ufos["word count"] = ufos["comments"].str.split().str.len()
ufos
Out[35]:
city state country shape duration (seconds) duration (hours/min) comments date posted latitude longitude word count
datetime
10/10/1949 20:30 san marcos tx us cylinder 2700.0 45 minutes This event took place in early fall around 194... 4/27/2004 29.883056 -97.941111 24
10/10/1949 21:00 lackland afb tx NaN light 7200.0 1-2 hrs 1949 Lackland AFB&#44 TX. Lights racing acros... 12/16/2005 29.384210 -98.581082 17
10/10/1955 17:00 chester (uk/england) NaN gb circle 20.0 20 seconds Green/Orange circular disc over Chester&#44 En... 1/21/2008 53.200000 -2.916667 6
10/10/1956 21:00 edna tx us circle 20.0 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.978333 -96.645833 26
10/10/1960 20:00 kaneohe hi us light 900.0 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 -157.803611 25
... ... ... ... ... ... ... ... ... ... ... ...
10/12/1982 05:30 pearlington ms us light 29.0 29 seconds Round light which was observed going into spac... 12/12/2013 30.246389 -89.611111 18
10/12/1985 23:45 swansboro nc us disk 300.0 23:45 - 23:50 My sister and I observed a disk for about 5 mi... 12/2/2000 34.687500 -77.119444 16
10/12/1988 16:30 melbourne (vic&#44 australia) NaN au cigar 900.0 15 min&#39s Large cigar shaped craft&#44flying sideways&#4... 9/29/2004 -37.813938 144.963425 8
10/12/1994 11:55 schenectady ny us other 120.0 a few minutes I had just picked up my Daugther (2yrs old) fr... 4/1/2001 42.814167 -73.940000 28
10/12/1994 15:00 monticello ky us chevron 120.0 1-2 minutes Triangular/chevron small object with fixed lig... 10/30/2006 36.829722 -84.849167 10

1001 rows × 11 columns

Apply your own functions¶

So what if you want to call your own functions on each element? Call the apply(...) function on a Series or DataFrame and pass in another function as an argument. Let's try writing a program that can remove the trailing parentheticals in the city name for the UFO dataset.

In [36]:
def clean_city_name(s):
    """
    Returns all the characters in the given string with trailing parentheticals removed.

    >>> clean_city_name("seattle (ballard area)")
    'seattle'
    >>> clean_city_name("seattle (west)")
    'seattle'
    >>> clean_city_name("melbourne (vic&#44 australia)")
    'melbourne'
    >>> clean_city_name("chester (uk/england)")
    'chester'
    >>> clean_city_name("carrieres sous poissy (france)")
    'carrieres sous poissy'
    >>> clean_city_name("seattle")
    'seattle'
    """
    index = s.find("(")
    if index == -1:
        return s
    return s[:index].rstrip()


doctest.run_docstring_examples(clean_city_name, globals())
In [37]:
ufos["city"].apply(clean_city_name).value_counts() # like groupby("city").count()
Out[37]:
city
new york city     12
seattle           12
tinley park        8
oak forest         8
las vegas          7
                  ..
holiday            1
eagan              1
siloam sprngs      1
canyonlands np     1
monticello         1
Name: count, Length: 774, dtype: int64

In practice, this can be useful for carrying-out data cleaning tasks such as removing punctuation or converting special characters. apply lets us write and test a function that achieves our task on a single string, and then apply that function to every string in a dataset.

short review¶

In [ ]:
.groupby(["column1", "column2"])
.loc[row_indexers, column_indexers]
.loc[(v1, v2, slice(1,5))]
["city"].apply(your_method_for_cleaning_data)

https://pandas.pydata.org/docs/reference/frame.html Pandas Documentation