According to the table above, it shows that the values of Survived column are either 0 or 1, where 0 represents that the passenger is not survived while 1 says that they are survived. Now in order to find out the number of the two, we are going to employ groupby() method like this:
survived_count = df.groupby('Survived')['Survived'].count()
survived_count
Here’s how to read it: “Group the data frame by values in Survived column, and count the number of occurrences of each group.”
In this case, since the Survived only has 2 possible values (either 0 or 1), then the code above produces two groups. If we print out survived_count variable, it will produce the following output:
Survived
0 549
1 342
Name: Survived, dtype: int64
Based on the output above, we can see that there are 549 people who were not survived. To make things look better, I wanna display these numbers in form of graph. Here I will use bar() function coming from Matplotlib module. The function is pretty easy to understand. The two parameters that we need to pass is just the index name and its values.
plt.figure(figsize=(4,5))
plt.bar(survived_count.index, survived_count.values)
plt.title('Grouped by survival')
plt.xticks([0,1],['Not survived', 'Survived'])for i, value in enumerate(survived_count.values):
plt.text(i, value-70, str(value), fontsize=12, color='white',
horizontalalignment='center', verticalalignment='center')plt.show()
And here is the output:
Now I will also do the similar thing in order to find out the number of survived persons based on their gender. Notice that here I use sum() instead of count() because we are only interested to calculate the number of survived passengers which are represented by number 1. So it’s kinda like adding 1s in each group.
survived_sex = df.groupby('Sex')['Survived'].sum()plt.figure(figsize=(4,5))
plt.bar(survived_sex.index, survived_sex.values)
plt.title('Survived female and male')for i, value in enumerate(survived_sex.values):
plt.text(i, value-20, str(value), fontsize=12, color='white',
horizontalalignment='center', verticalalignment='center')plt.show()
Well, I think the graph above is pretty straightforward to understand 🙂
Next, I wanna find out the distribution of ticket classes where the attribute is stored at Pclass column. The way to do it is pretty much similar to the one I created earlier.
pclass_count = df.groupby('Pclass')['Pclass'].count()
Now that there are 3 values stored in pclass_count variable in which each of those represents the number of tickets in each class. However, instead of printing out a graph here I prefer to display it in form of pie chart using pie() function.
plt.figure(figsize=(7,7))
plt.title(‘Grouped by pclass’)
plt.pie(pclass_count.values, labels=[‘Class 1’, ‘Class 2’, ‘Class 3’],
autopct=’%1.1f%%’, textprops={‘fontsize’:13})plt.show()
Furthermore, we can also display gender and embarkation distribution pie chart using the exact same method.
Another thing that I wanna find out is the age distribution. Before I go further, remember that our Age column contains 177 missing values out of 891 data in total. Therefore, we need to get rid of those NaNs first. Here’s my approach to do it:
ages = df[df['Age'].notnull()]['Age'].values
What I am actually doing in the code above is just to retrieve all non-NaN age values and then store the result to ages Numpy array. Next, I will use histogram() function taken from Numpy module. Notice that here I pass two arguments to the function: ages array and a list of bins.
ages_hist = np.histogram(ages, bins=[0,10,20,30,40,50,60,70,80,90])
ages_hist
After running the code above, we should get the following output:
(array([ 62, 102, 220, 167, 89, 48, 19, 6, 1], dtype=int64),
array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]))
It’s important to know that the output value of np.histogram() function above is a tuple with 2 elements, where the first one holds the number of data in each bin while the second one is the bins itself. To make things clearer in the figure, I will also define labels in ages_hist_labels.
ages_hist_labels = [‘0–10’, ‘11–20’, ‘21–30’, ‘31–40’, ‘41–50’, ‘51–60’, ‘61–70’, ‘71–80’, ‘81–90’]
And finally we can show the histogram like this:
plt.figure(figsize=(7,7))
plt.title('Age distribution')
plt.bar(ages_hist_labels, ages_hist[0])
plt.xlabel('Age')
plt.ylabel('No of passenger')for i, bin in zip(ages_hist[0], range(9)):
plt.text(bin, i+3, str(int(i)), fontsize=12,
horizontalalignment='center', verticalalignment='center')plt.show()
If we pay attention to our Cabin column, we can see that all non-NaN values are always started with a capital letter which then followed by several numbers. This can be checked using df[‘Cabin’].unique()[:10] command. Here I only return the first 10 unique values for simplicity.
array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',
'C23 C25 C27', 'B78'], dtype=object)
I got a feeling that probably these initial letters might contain something important, so then I decided to take it and leave the numbers. In order to do that, we need to create a function called take_initial().
def take_initial(x):
return x[0]
The function above is pretty straightforward though. The argument x essentially represents a string of each row in which we will return only its initial character. Before applying the function to all rows in the Cabin column, we need to drop all NaN values first and store it in cabins object like this:
cabins = df['Cabin'].dropna()
Now as the null values have been removed, we can start to apply the take_initial() function and directly updating the contents of cabins:
cabins = cabins.apply(take_initial)
Next we will use value_counts() method to find out the number of occurrences of each letter. I will also directly store its values in cabins_count object.
cabins_count = cabins.value_counts()
cabins_count
After running the code above we are going to see the following output.
C 59
B 47
D 33
E 32
A 15
F 13
G 4
T 1
Name: Cabin, dtype: int64
Finally, to make things look better, I will use plt.bar() again to display it in form of bar chart.
plt.title('Cabin distribution')
plt.bar(cabins_count.index, cabins_count.values)
plt.show()
Fare attributes might also play an important role to predict whether a passenger is survived. Different to the previous figures, here instead of using bar or pie chart, I will create a boxplot. Fortunately, it’s extremely simple to do that as basically it can be shown just by using plt.boxplot() function.
plt.figure(figsize=(13,1))
plt.title(‘Fare distribution’)
plt.boxplot(df[‘Fare’], vert=False)
plt.show()
Credit: BecomingHuman By: Muhammad Ardi
