Data Analysis Data Wrangling Tutorial

value_counts() Method: Count Unique Occurrences of Values in a Column

In pandas, for a column in a DataFrame, we can use the value_counts() method to easily count the unique occurences of values.

There's additional interesting analyis we can do with value_counts() too. We'll try them out using the titanic dataset.

Import Module

In [27]:
import pandas as pd
import seaborn as sns

Get Titanic Dataset

We'll use the titanic dataset included in the seaborn library.

In [28]:
df = sns.load_dataset('titanic')

Below is a preview of the first few rows of the dataset.

Each row includes details of a person who boarded the famous Titanic cruise ship.

In this tutorial, we're just going to utilize the sex and fare columns. The sex column classifies the person's gender as male or female. The fare column indicates the dollar amount each person paid to board the Titanic.

In [29]:
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True

Fun with value_counts()

Here is the simple use of value_counts() we call on the sex column that returns us the count of occurences of each of the unique values in this column.

In [30]:
male      577
female    314
Name: sex, dtype: int64

Now, we want to do the same operation, but this time sort our outputted values in the sex column, male and female, so that values that start with the letter a appear at the top and values that start with letter z appear at the bottom. This is considered ascending order.

f is before m in the alphabet so we see female before male.

In our value_counts method, we'll set the argument ascending to True.

In [31]:
female    314
male      577
Name: sex, dtype: int64

Often times, we want to know what percentage of the whole is for each value that appears in the column. For example, if we took the two counts above, 577 and 314 and we sum them up, we'd get 891. So, what percentage of people on the titanic were male. The calculation is 577/891 x 100 = 64.75%.

To calculate this in pandas with the value_counts() method, set the argument normalize to True.

In [32]:
male      0.647587
female    0.352413
Name: sex, dtype: float64

Before we try a new value_counts() argument, let's take a look at some basic descriptive statistics of the fare column. To accomplish this, we'll call the describe() method on the column.

There's 891 values of fare data, a mean of 32 and a standard deviation of 49 which indicates a fairly wide spread of data.

In [33]:
count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: fare, dtype: float64

Another interesting feature of the value_counts() method is that it can be used to bin continuous data into discrete intervals. We set the argument bins to an integer representing the number of bins to create.

For each bin, the range of fare amounts in dollar values is the same. One contains fares from 73.19 to 146.38 which is a range of 73.19. Another bin contains fares from 146.38 to 73.19 which is also a range of 73.19. See how the ranges are same! However, inside each range of fare values can contain a different count of the number of tickets bought by passengers of the Titanic.

We can see most people paid under 73.19 for their ticket.

In [34]:
(-0.513, 73.19]       789
(73.19, 146.38]        71
(146.38, 219.57]       15
(219.57, 292.76]       13
(439.139, 512.329]      3
(365.949, 439.139]      0
(292.76, 365.949]       0
Name: fare, dtype: int64