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03-Useful-Methods

Updated
17 Sept 2026
2 min read
8 sections
Page 16 of 117
Python & Machine Learning

Useful Methods

Let's cover some useful methods and functions built in to pandas. This is actually just a small sampling of the functions and methods available in Pandas, but they are some of the most commonly used. The documentation is a great resource to continue exploring more methods and functions (we will introduce more further along in the course). Here is a list of functions and methods we'll cover here (click on one to jump to that section in this notebook.):

Make sure to view the video lessons to get the full explanation!

The .apply() method

Here we will learn about a very useful method known as apply on a DataFrame. This allows us to apply and broadcast custom functions on a DataFrame column

python
df = pd.read_csv('tips.csv')
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
66 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251

apply with a function

python
df.info()
Out · stdout
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 11 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----
13 more lines
 0   total_bill        244 non-null    float64
 1   tip               244 non-null    float64
 2   sex               244 non-null    object 
 3   smoker            244 non-null    object 
 4   day               244 non-null    object 
 5   time              244 non-null    object 
 6   size              244 non-null    int64  
 7   price_per_person  244 non-null    float64
 8   Payer Name        244 non-null    object 
 9   CC Number         244 non-null    int64  
 10  Payment ID        244 non-null    object 
dtypes: float64(3), int64(2), object(6)
memory usage: 21.1+ KB
python
def last_four(num):
    return str(num)[-4:]
python
df['CC Number'][0]
Out · result
3560325168603410
python
last_four(3560325168603410)
Out · result
'3410'
python
df['last_four'] = df['CC Number'].apply(last_four)
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
72 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221

Using .apply() with more complex functions

python
df['total_bill'].mean()
Out · result
19.78594262295082
python
def yelp(price):
    if price < 10:
        return '$'
    elif price >= 10 and price < 30:
        return '$$'
    else:
        return '$$$'
python
df['Expensive'] = df['total_bill'].apply(yelp)
python
# df

apply with lambda

python
def simple(num):
    return num*2
python
lambda num: num*2
Out · result
<function __main__.<lambda>(num)>
python
df['total_bill'].apply(lambda bill:bill*0.18)
Out · result
0      3.0582
1      1.8612
2      3.7818
3      4.2624
4      4.4262
7 more lines
        ...  
239    5.2254
240    4.8924
241    4.0806
242    3.2076
243    3.3804
Name: total_bill, Length: 244, dtype: float64

apply that uses multiple columns

Note, there are several ways to do this:

https://stackoverflow.com/questions/19914937/applying-function-with-multiple-arguments-to-create-a-new-pandas-column

python
df.head()
Out · table
total_bill
tip
sex
smoker
day
78 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
python
def quality(total_bill,tip):
    if tip/total_bill  > 0.25:
        return "Generous"
    else:
        return "Other"
python
df['Tip Quality'] = df[['total_bill','tip']].apply(lambda df: quality(df['total_bill'],df['tip']),axis=1)
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Other
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Other
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Other
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Other
python
df['Tip Quality'] = np.vectorize(quality)(df['total_bill'], df['tip'])
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Other
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Other
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Other
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Other

So, which one is faster?

python
import timeit 
  
# code snippet to be executed only once 
setup = '''
import numpy as np
import pandas as pd
df = pd.read_csv('tips.csv')
def quality(total_bill,tip):
    if tip/total_bill  > 0.25:
        return "Generous"
    else:
        return "Other"
'''
  
# code snippet whose execution time is to be measured 
stmt_one = ''' 
df['Tip Quality'] = df[['total_bill','tip']].apply(lambda df: quality(df['total_bill'],df['tip']),axis=1)
'''

stmt_two = '''
df['Tip Quality'] = np.vectorize(quality)(df['total_bill'], df['tip'])
'''
python
timeit.timeit(setup = setup, 
                    stmt = stmt_one, 
                    number = 1000)
Out · result
5.0198852999999986
python
timeit.timeit(setup = setup, 
                    stmt = stmt_two, 
                    number = 1000)
Out · result
0.21840849999999534

Wow! Vectorization is much faster! Keep np.vectorize() in mind for the future.

Full Details: https://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html

df.describe for statistical summaries

python
df.describe()
Out · table
total_bill
tip
size
price_per_person
CC Number
48 more lines
count
244.000000
244.000000
244.000000
244.000000
2.440000e+02
mean
19.785943
2.998279
2.569672
7.888197
2.563496e+15
std
8.902412
1.383638
0.951100
2.914234
2.369340e+15
min
3.070000
1.000000
1.000000
2.880000
6.040679e+10
25%
13.347500
2.000000
2.000000
5.800000
3.040731e+13
50%
17.795000
2.900000
2.000000
7.255000
3.525318e+15
75%
24.127500
3.562500
3.000000
9.390000
4.553675e+15
max
50.810000
10.000000
6.000000
20.270000
6.596454e+15
python
df.describe().transpose()
Out · table
count
mean
std
min
25%
48 more lines
50%
75%
max
total_bill
244.0
1.978594e+01
8.902412e+00
3.070000e+00
1.334750e+01
1.779500e+01
2.412750e+01
5.081000e+01
tip
244.0
2.998279e+00
1.383638e+00
1.000000e+00
2.000000e+00
2.900000e+00
3.562500e+00
1.000000e+01
size
244.0
2.569672e+00
9.510998e-01
1.000000e+00
2.000000e+00
2.000000e+00
3.000000e+00
6.000000e+00
price_per_person
244.0
7.888197e+00
2.914234e+00
2.880000e+00
5.800000e+00
7.255000e+00
9.390000e+00
2.027000e+01
CC Number
244.0
2.563496e+15
2.369340e+15
6.040679e+10
3.040731e+13
3.525318e+15
4.553675e+15
6.596454e+15

sort_values()

python
df.sort_values('tip')
Out · table
total_bill
tip
sex
smoker
day
175 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
67
3.07
1.00
Female
Yes
Sat
Dinner
1
3.07
Tiffany Brock
4359488526995267
Sat3455
5267
$
Generous
236
12.60
1.00
Male
Yes
Sat
Dinner
2
6.30
Matthew Myers
3543676378973965
Sat5032
3965
$$
Other
92
5.75
1.00
Female
Yes
Fri
Dinner
2
2.88
Leah Ramirez
3508911676966392
Fri3780
6392
$
Other
111
7.25
1.00
Female
No
Sat
Dinner
1
7.25
Terri Jones
3559221007826887
Sat4801
6887
$
Other
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
141
34.30
6.70
Male
No
Thur
Lunch
6
5.72
Steven Carlson
3526515703718508
Thur1025
8508
$$$
Other
59
48.27
6.73
Male
No
Sat
Dinner
4
12.07
Brian Ortiz
6596453823950595
Sat8139
0595
$$$
Other
23
39.42
7.58
Male
No
Sat
Dinner
4
9.86
Lance Peterson
3542584061609808
Sat239
9808
$$$
Other
212
48.33
9.00
Male
No
Sat
Dinner
4
12.08
Alex Williamson
676218815212
Sat4590
5212
$$$
Other
170
50.81
10.00
Male
Yes
Sat
Dinner
3
16.94
Gregory Clark
5473850968388236
Sat1954
8236
$$$
Other
244 rows × 14 columns
python
# Helpful if you want to reorder after a sort
# https://stackoverflow.com/questions/13148429/how-to-change-the-order-of-dataframe-columns
df.sort_values(['tip','size'])
Out · table
total_bill
tip
sex
smoker
day
175 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
67
3.07
1.00
Female
Yes
Sat
Dinner
1
3.07
Tiffany Brock
4359488526995267
Sat3455
5267
$
Generous
111
7.25
1.00
Female
No
Sat
Dinner
1
7.25
Terri Jones
3559221007826887
Sat4801
6887
$
Other
92
5.75
1.00
Female
Yes
Fri
Dinner
2
2.88
Leah Ramirez
3508911676966392
Fri3780
6392
$
Other
236
12.60
1.00
Male
Yes
Sat
Dinner
2
6.30
Matthew Myers
3543676378973965
Sat5032
3965
$$
Other
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
141
34.30
6.70
Male
No
Thur
Lunch
6
5.72
Steven Carlson
3526515703718508
Thur1025
8508
$$$
Other
59
48.27
6.73
Male
No
Sat
Dinner
4
12.07
Brian Ortiz
6596453823950595
Sat8139
0595
$$$
Other
23
39.42
7.58
Male
No
Sat
Dinner
4
9.86
Lance Peterson
3542584061609808
Sat239
9808
$$$
Other
212
48.33
9.00
Male
No
Sat
Dinner
4
12.08
Alex Williamson
676218815212
Sat4590
5212
$$$
Other
170
50.81
10.00
Male
Yes
Sat
Dinner
3
16.94
Gregory Clark
5473850968388236
Sat1954
8236
$$$
Other
244 rows × 14 columns

df.corr() for correlation checks

Wikipedia on Correlation

python
df.corr()
Out · table
total_bill
tip
size
price_per_person
CC Number
30 more lines
total_bill
1.000000
0.675734
0.598315
0.647554
0.104576
tip
0.675734
1.000000
0.489299
0.347405
0.110857
size
0.598315
0.489299
1.000000
-0.175359
-0.030239
price_per_person
0.647554
0.347405
-0.175359
1.000000
0.135240
CC Number
0.104576
0.110857
-0.030239
0.135240
1.000000
python
df[['total_bill','tip']].corr()
Out · table
total_bill
tip
total_bill
1.000000
0.675734
3 more lines
tip
0.675734
1.000000

idxmin and idxmax

python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Other
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Other
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Other
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Other
python
df['total_bill'].max()
Out · result
50.81
python
df['total_bill'].idxmax()
Out · result
170
python
df['total_bill'].idxmin()
Out · result
67
python
df.iloc[67]
Out · result
total_bill                      3.07
tip                                1
sex                           Female
smoker                           Yes
day                              Sat
10 more lines
time                          Dinner
size                               1
price_per_person                3.07
Payer Name             Tiffany Brock
CC Number           4359488526995267
Payment ID                   Sat3455
last_four                       5267
Expensive                          $
Tip Quality                 Generous
Name: 67, dtype: object
python
df.iloc[170]
Out · result
total_bill                     50.81
tip                               10
sex                             Male
smoker                           Yes
day                              Sat
10 more lines
time                          Dinner
size                               3
price_per_person               16.94
Payer Name             Gregory Clark
CC Number           5473850968388236
Payment ID                   Sat1954
last_four                       8236
Expensive                        $$$
Tip Quality                    Other
Name: 170, dtype: object

value_counts

Nice method to quickly get a count per category. Only makes sense on categorical columns.

python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Other
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Other
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Other
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Other
python
df['sex'].value_counts()
Out · result
Male      157
Female     87
Name: sex, dtype: int64

replace

Quickly replace values with another one.

python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Other
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Other
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Other
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Other
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Other
python
df['Tip Quality'].replace(to_replace='Other',value='Ok')
Out · result
0            Ok
1            Ok
2            Ok
3            Ok
4            Ok
57 more lines
5            Ok
6            Ok
7            Ok
8            Ok
9            Ok
10           Ok
11           Ok
12           Ok
13           Ok
14           Ok
15           Ok
16           Ok
17           Ok
18           Ok
19           Ok
20           Ok
21           Ok
22           Ok
23           Ok
24           Ok
25           Ok
26           Ok
27           Ok
28           Ok
29           Ok
         ...   
214          Ok
215          Ok
216          Ok
217          Ok
218          Ok
219          Ok
220          Ok
221    Generous
222          Ok
223          Ok
224          Ok
225          Ok
226          Ok
227          Ok
228          Ok
229          Ok
230          Ok
231          Ok
232    Generous
233          Ok
234          Ok
235          Ok
236          Ok
237          Ok
238          Ok
239          Ok
240          Ok
241          Ok
242          Ok
243          Ok
Name: Tip Quality, Length: 244, dtype: object
python
df['Tip Quality'] = df['Tip Quality'].replace(to_replace='Other',value='Ok')
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Ok
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Ok
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Ok
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Ok
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Ok

unique

python
df['size'].unique()
Out · result
array([2, 3, 4, 1, 6, 5], dtype=int64)
python
df['size'].nunique()
Out · result
6
python
df['time'].unique()
Out · result
array(['Dinner', 'Lunch'], dtype=object)

map

python
my_map = {'Dinner':'D','Lunch':'L'}
python
df['time'].map(my_map)
Out · result
0      D
1      D
2      D
3      D
4      D
57 more lines
5      D
6      D
7      D
8      D
9      D
10     D
11     D
12     D
13     D
14     D
15     D
16     D
17     D
18     D
19     D
20     D
21     D
22     D
23     D
24     D
25     D
26     D
27     D
28     D
29     D
      ..
214    D
215    D
216    D
217    D
218    D
219    D
220    L
221    L
222    L
223    L
224    L
225    L
226    L
227    D
228    D
229    D
230    D
231    D
232    D
233    D
234    D
235    D
236    D
237    D
238    D
239    D
240    D
241    D
242    D
243    D
Name: time, Length: 244, dtype: object
python
df.head()
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Ok
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Ok
2
21.01
3.50
Male
No
Sun
Dinner
3
7.00
Travis Walters
6011812112971322
Sun4458
1322
$$
Ok
3
23.68
3.31
Male
No
Sun
Dinner
2
11.84
Nathaniel Harris
4676137647685994
Sun5260
5994
$$
Ok
4
24.59
3.61
Female
No
Sun
Dinner
4
6.15
Tonya Carter
4832732618637221
Sun2251
7221
$$
Ok

Duplicates

.duplicated() and .drop_duplicates()

python
# Returns True for the 1st instance of a duplicated row
df.duplicated()
Out · result
0      False
1      False
2      False
3      False
4      False
57 more lines
5      False
6      False
7      False
8      False
9      False
10     False
11     False
12     False
13     False
14     False
15     False
16     False
17     False
18     False
19     False
20     False
21     False
22     False
23     False
24     False
25     False
26     False
27     False
28     False
29     False
       ...  
214    False
215    False
216    False
217    False
218    False
219    False
220    False
221    False
222    False
223    False
224    False
225    False
226    False
227    False
228    False
229    False
230    False
231    False
232    False
233    False
234    False
235    False
236    False
237    False
238    False
239    False
240    False
241    False
242    False
243    False
Length: 244, dtype: bool
python
simple_df = pd.DataFrame([1,2,2],['a','b','c'])
python
simple_df
Out · table
0
a
1
b
2
2 more lines
c
2
python
simple_df.duplicated()
Out · result
a    False
b    False
c     True
dtype: bool
python
simple_df.drop_duplicates()
Out · table
0
a
1
b
2

between

left: A scalar value that defines the left boundary right: A scalar value that defines the right boundary inclusive: A Boolean value which is True by default. If False, it excludes the two passed arguments while checking.

python
df['total_bill'].between(10,20,inclusive=True)
Out · result
0       True
1       True
2      False
3      False
4      False
57 more lines
5      False
6      False
7      False
8       True
9       True
10      True
11     False
12      True
13      True
14      True
15     False
16      True
17      True
18      True
19     False
20      True
21     False
22      True
23     False
24      True
25      True
26      True
27      True
28     False
29      True
       ...  
214    False
215     True
216    False
217     True
218    False
219    False
220     True
221     True
222    False
223     True
224     True
225     True
226     True
227    False
228     True
229    False
230    False
231     True
232     True
233     True
234     True
235     True
236     True
237    False
238    False
239    False
240    False
241    False
242     True
243     True
Name: total_bill, Length: 244, dtype: bool
python
df[df['total_bill'].between(10,20,inclusive=True)]
Out · table
total_bill
tip
sex
smoker
day
925 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
0
16.99
1.01
Female
No
Sun
Dinner
2
8.49
Christy Cunningham
3560325168603410
Sun2959
3410
$$
Ok
1
10.34
1.66
Male
No
Sun
Dinner
3
3.45
Douglas Tucker
4478071379779230
Sun4608
9230
$$
Ok
8
15.04
1.96
Male
No
Sun
Dinner
2
7.52
Joseph Mcdonald
3522866365840377
Sun6820
0377
$$
Ok
9
14.78
3.23
Male
No
Sun
Dinner
2
7.39
Jerome Abbott
3532124519049786
Sun3775
9786
$$
Ok
10
10.27
1.71
Male
No
Sun
Dinner
2
5.14
William Riley
566287581219
Sun2546
1219
$$
Ok
12
15.42
1.57
Male
No
Sun
Dinner
2
7.71
Chad Harrington
577040572932
Sun1300
2932
$$
Ok
13
18.43
3.00
Male
No
Sun
Dinner
4
4.61
Joshua Jones
6011163105616890
Sun2971
6890
$$
Ok
14
14.83
3.02
Female
No
Sun
Dinner
2
7.42
Vanessa Jones
30016702287574
Sun3848
7574
$$
Ok
16
10.33
1.67
Female
No
Sun
Dinner
3
3.44
Elizabeth Foster
4240025044626033
Sun9715
6033
$$
Ok
17
16.29
3.71
Male
No
Sun
Dinner
3
5.43
John Pittman
6521340257218708
Sun2998
8708
$$
Ok
18
16.97
3.50
Female
No
Sun
Dinner
3
5.66
Laura Martinez
30422275171379
Sun2789
1379
$$
Ok
20
17.92
4.08
Male
No
Sat
Dinner
2
8.96
Thomas Rice
4403296224639756
Sat1709
9756
$$
Ok
22
15.77
2.23
Female
No
Sat
Dinner
2
7.88
Ashley Shelton
3524119516293213
Sat9786
3213
$$
Ok
24
19.82
3.18
Male
No
Sat
Dinner
2
9.91
Christopher Ross
36739148167928
Sat6236
7928
$$
Ok
25
17.81
2.34
Male
No
Sat
Dinner
4
4.45
Robert Perkins
30502930499388
Sat907
9388
$$
Ok
26
13.37
2.00
Male
No
Sat
Dinner
2
6.68
Kyle Avery
6531339539615499
Sat6651
5499
$$
Ok
27
12.69
2.00
Male
No
Sat
Dinner
2
6.34
Patrick Barber
30155551880343
Sat394
0343
$$
Ok
29
19.65
3.00
Female
No
Sat
Dinner
2
9.82
Melinda Murphy
5489272944576051
Sat2467
6051
$$
Ok
31
18.35
2.50
Male
No
Sat
Dinner
4
4.59
Danny Santiago
630415546013
Sat4947
6013
$$
Ok
32
15.06
3.00
Female
No
Sat
Dinner
2
7.53
Amanda Wilson
213186304291560
Sat1327
1560
$$
Ok
34
17.78
3.27
Male
No
Sat
Dinner
2
8.89
Jacob Castillo
3551492000704805
Sat8124
4805
$$
Ok
36
16.31
2.00
Male
No
Sat
Dinner
3
5.44
William Ford
3527691170179398
Sat9139
9398
$$
Ok
37
16.93
3.07
Female
No
Sat
Dinner
3
5.64
Erin Lewis
5161695527390786
Sat6406
0786
$$
Ok
38
18.69
2.31
Male
No
Sat
Dinner
3
6.23
Brandon Bradley
4427601595688633
Sat4056
8633
$$
Ok
40
16.04
2.24
Male
No
Sat
Dinner
3
5.35
Adam Edwards
3544447755679420
Sat8549
9420
$$
Ok
41
17.46
2.54
Male
No
Sun
Dinner
2
8.73
David Boyer
3536678244278149
Sun9460
8149
$$
Ok
42
13.94
3.06
Male
No
Sun
Dinner
2
6.97
Bryan Brown
36231182760859
Sun1699
0859
$$
Ok
45
18.29
3.00
Male
No
Sun
Dinner
2
9.14
Richard Fitzgerald
375156610762053
Sun8643
2053
$$
Ok
49
18.04
3.00
Male
No
Sun
Dinner
2
9.02
William Roth
6573923967142503
Sun9774
2503
$$
Ok
50
12.54
2.50
Male
No
Sun
Dinner
2
6.27
Jeremiah Neal
2225400829691416
Sun2021
1416
$$
Ok
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
191
19.81
4.19
Female
Yes
Thur
Lunch
2
9.90
Kristy Boyd
4317015327600068
Thur967
0068
$$
Ok
193
15.48
2.02
Male
Yes
Thur
Lunch
2
7.74
Raymond Sullivan
180068856139315
Thur606
9315
$$
Ok
194
16.58
4.00
Male
Yes
Thur
Lunch
2
8.29
Benjamin Weber
676210011505
Thur9318
1505
$$
Ok
196
10.34
2.00
Male
Yes
Thur
Lunch
2
5.17
Eric Martin
30442491190342
Thur9862
0342
$$
Ok
198
13.00
2.00
Female
Yes
Thur
Lunch
2
6.50
Katherine Bond
4926725945192
Thur437
5192
$$
Ok
199
13.51
2.00
Male
Yes
Thur
Lunch
2
6.76
Joseph Murphy MD
6547218923471275
Thur2428
1275
$$
Ok
200
18.71
4.00
Male
Yes
Thur
Lunch
3
6.24
Jason Conrad
4581233003487
Thur6048
3487
$$
Ok
201
12.74
2.01
Female
Yes
Thur
Lunch
2
6.37
Abigail Parks
3586645396220590
Thur2544
0590
$$
Ok
202
13.00
2.00
Female
Yes
Thur
Lunch
2
6.50
Ashley Shaw
180088043008041
Thur1301
8041
$$
Ok
203
16.40
2.50
Female
Yes
Thur
Lunch
2
8.20
Toni Brooks
3582289985920239
Thur7770
0239
$$
Ok
205
16.47
3.23
Female
Yes
Thur
Lunch
3
5.49
Carly Reyes
4787787236486
Thur8084
6486
$$
Ok
209
12.76
2.23
Female
Yes
Sat
Dinner
2
6.38
Sarah Cunningham
341876516331163
Sat1274
1163
$$
Ok
213
13.27
2.50
Female
Yes
Sat
Dinner
2
6.64
Robin Andersen
580140531089
Sat1374
1089
$$
Ok
215
12.90
1.10
Female
Yes
Sat
Dinner
2
6.45
Jessica Owen
4726904879471
Sat6983
9471
$$
Ok
217
11.59
1.50
Male
Yes
Sat
Dinner
2
5.80
Gary Orr
30324521283406
Sat8489
3406
$$
Ok
220
12.16
2.20
Male
Yes
Fri
Lunch
2
6.08
Ricky Johnson
213109508670736
Fri4607
0736
$$
Ok
221
13.42
3.48
Female
Yes
Fri
Lunch
2
6.71
Leslie Kaufman
379437981958785
Fri7511
8785
$$
Generous
223
15.98
3.00
Female
No
Fri
Lunch
3
5.33
Mary Rivera
5343428579353069
Fri6014
3069
$$
Ok
224
13.42
1.58
Male
Yes
Fri
Lunch
2
6.71
Ronald Vaughn DVM
341503466406403
Fri5959
6403
$$
Ok
225
16.27
2.50
Female
Yes
Fri
Lunch
2
8.14
Whitney Arnold
3579111947217428
Fri6665
7428
$$
Ok
226
10.09
2.00
Female
Yes
Fri
Lunch
2
5.04
Ruth Weiss
5268689490381635
Fri6359
1635
$$
Ok
228
13.28
2.72
Male
No
Sat
Dinner
2
6.64
Glenn Jones
502061651712
Sat2937
1712
$$
Ok
231
15.69
3.00
Male
Yes
Sat
Dinner
3
5.23
Jason Parks
4812333796161
Sat6334
6161
$$
Ok
232
11.61
3.39
Male
No
Sat
Dinner
2
5.80
James Taylor
6011482917327995
Sat2124
7995
$$
Generous
233
10.77
1.47
Male
No
Sat
Dinner
2
5.38
Paul Novak
6011698897610858
Sat1467
0858
$$
Ok
234
15.53
3.00
Male
Yes
Sat
Dinner
2
7.76
Tracy Douglas
4097938155941930
Sat7220
1930
$$
Ok
235
10.07
1.25
Male
No
Sat
Dinner
2
5.04
Sean Gonzalez
3534021246117605
Sat4615
7605
$$
Ok
236
12.60
1.00
Male
Yes
Sat
Dinner
2
6.30
Matthew Myers
3543676378973965
Sat5032
3965
$$
Ok
242
17.82
1.75
Male
No
Sat
Dinner
2
8.91
Dennis Dixon
4375220550950
Sat17
0950
$$
Ok
243
18.78
3.00
Female
No
Thur
Dinner
2
9.39
Michelle Hardin
3511451626698139
Thur672
8139
$$
Ok
130 rows × 14 columns

sample

python
df.sample(5)
Out · table
total_bill
tip
sex
smoker
day
84 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
216
28.15
3.00
Male
Yes
Sat
Dinner
5
5.63
Shawn Barnett PhD
4590982568244
Sat7320
8244
$$
Ok
136
10.33
2.00
Female
No
Thur
Lunch
2
5.16
Donna Kelly
180048553626376
Thur1393
6376
$$
Ok
13
18.43
3.00
Male
No
Sun
Dinner
4
4.61
Joshua Jones
6011163105616890
Sun2971
6890
$$
Ok
146
18.64
1.36
Female
No
Thur
Lunch
3
6.21
Kelly Estrada
60463302327
Thur3941
2327
$$
Ok
56
38.01
3.00
Male
Yes
Sat
Dinner
4
9.50
James Christensen DDS
349793629453226
Sat8903
3226
$$$
Ok
python
df.sample(frac=0.1)
Out · table
total_bill
tip
sex
smoker
day
369 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
73
25.28
5.00
Female
Yes
Sat
Dinner
2
12.64
Julie Holmes
5418689346409571
Sat6065
9571
$$
Ok
141
34.30
6.70
Male
No
Thur
Lunch
6
5.72
Steven Carlson
3526515703718508
Thur1025
8508
$$$
Ok
239
29.03
5.92
Male
No
Sat
Dinner
3
9.68
Michael Avila
5296068606052842
Sat2657
2842
$$
Ok
237
32.83
1.17
Male
Yes
Sat
Dinner
2
16.42
Thomas Brown
4284722681265508
Sat2929
5508
$$$
Ok
69
15.01
2.09
Male
Yes
Sat
Dinner
2
7.50
Adam Hall
4700924377057571
Sat855
7571
$$
Ok
108
18.24
3.76
Male
No
Sat
Dinner
2
9.12
Steven Grant
4112810433473856
Sat6376
3856
$$
Ok
85
34.83
5.17
Female
No
Thur
Lunch
4
8.71
Shawna Cook
6011787464177340
Thur7972
7340
$$$
Ok
156
48.17
5.00
Male
No
Sun
Dinner
6
8.03
Ryan Gonzales
3523151482063321
Sun7518
3321
$$$
Ok
196
10.34
2.00
Male
Yes
Thur
Lunch
2
5.17
Eric Martin
30442491190342
Thur9862
0342
$$
Ok
41
17.46
2.54
Male
No
Sun
Dinner
2
8.73
David Boyer
3536678244278149
Sun9460
8149
$$
Ok
236
12.60
1.00
Male
Yes
Sat
Dinner
2
6.30
Matthew Myers
3543676378973965
Sat5032
3965
$$
Ok
225
16.27
2.50
Female
Yes
Fri
Lunch
2
8.14
Whitney Arnold
3579111947217428
Fri6665
7428
$$
Ok
61
13.81
2.00
Male
Yes
Sat
Dinner
2
6.90
Ryan Hernandez
4766834726806
Sat3030
6806
$$
Ok
203
16.40
2.50
Female
Yes
Thur
Lunch
2
8.20
Toni Brooks
3582289985920239
Thur7770
0239
$$
Ok
5
25.29
4.71
Male
No
Sun
Dinner
4
6.32
Erik Smith
213140353657882
Sun9679
7882
$$
Ok
220
12.16
2.20
Male
Yes
Fri
Lunch
2
6.08
Ricky Johnson
213109508670736
Fri4607
0736
$$
Ok
119
24.08
2.92
Female
No
Thur
Lunch
4
6.02
Melanie Jordan
676212062720
Thur8063
2720
$$
Ok
96
27.28
4.00
Male
Yes
Fri
Dinner
2
13.64
Eric Carter
4563054452787961
Fri3159
7961
$$
Ok
159
16.49
2.00
Male
No
Sun
Dinner
4
4.12
Christopher Soto
30501814271434
Sun1781
1434
$$
Ok
26
13.37
2.00
Male
No
Sat
Dinner
2
6.68
Kyle Avery
6531339539615499
Sat6651
5499
$$
Ok
129
22.82
2.18
Male
No
Thur
Lunch
3
7.61
Raymond Torres
4855776744024
Thur9424
4024
$$
Ok
21
20.29
2.75
Female
No
Sat
Dinner
2
10.14
Natalie Gardner
5448125351489749
Sat9618
9749
$$
Ok
94
22.75
3.25
Female
No
Fri
Dinner
2
11.38
Jamie Garza
676318332068
Fri2318
2068
$$
Ok
39
31.27
5.00
Male
No
Sat
Dinner
3
10.42
Mr. Brandon Berry
6011525851069856
Sat6373
9856
$$$
Ok

nlargest and nsmallest

python
df.nlargest(10,'tip')
Out · table
total_bill
tip
sex
smoker
day
159 more lines
time
size
price_per_person
Payer Name
CC Number
Payment ID
last_four
Expensive
Tip Quality
170
50.81
10.00
Male
Yes
Sat
Dinner
3
16.94
Gregory Clark
5473850968388236
Sat1954
8236
$$$
Ok
212
48.33
9.00
Male
No
Sat
Dinner
4
12.08
Alex Williamson
676218815212
Sat4590
5212
$$$
Ok
23
39.42
7.58
Male
No
Sat
Dinner
4
9.86
Lance Peterson
3542584061609808
Sat239
9808
$$$
Ok
59
48.27
6.73
Male
No
Sat
Dinner
4
12.07
Brian Ortiz
6596453823950595
Sat8139
0595
$$$
Ok
141
34.30
6.70
Male
No
Thur
Lunch
6
5.72
Steven Carlson
3526515703718508
Thur1025
8508
$$$
Ok
183
23.17
6.50
Male
Yes
Sun
Dinner
4
5.79
Dr. Michael James
4718501859162
Sun6059
9162
$$
Generous
214
28.17
6.50
Female
Yes
Sat
Dinner
3
9.39
Marissa Jackson
4922302538691962
Sat3374
1962
$$
Ok
47
32.40
6.00
Male
No
Sun
Dinner
4
8.10
James Barnes
3552002592874186
Sun9677
4186
$$$
Ok
239
29.03
5.92
Male
No
Sat
Dinner
3
9.68
Michael Avila
5296068606052842
Sat2657
2842
$$
Ok
88
24.71
5.85
Male
No
Thur
Lunch
2
12.36
Roger Taylor
4410248629955
Thur9003
9955
$$
Ok