generekxx / ds-eda-interpolation-nyc-ds-091018

Exploratory Data Analysis

Typically, whenever we are handed a new dataset or problem our first step is to begin to wrap our head around the problem and the data associated with it. This is where some of our previous tools such as the measure of center and dispersion will come in handy. From there, we can continue to dissect the problem by employing various techniques and algorithms to further analyze the dataset from various angles.

Here we'll investigate a classic dataset concerning the Titanic.

First let's import the dataset, see how long it is and preview the first 5 rows

import pandas as pd

df = pd.read_csv('train.csv')
print(len(df))
df.head()

891

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	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

Then let's quickly get some more info about each of our column features:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

We can also quickly visualize the data:

Along the diagonal are the distributions for each of our variables. Every other cell shows a correlation plot of one feature against another feature.

%matplotlib inline

pd.plotting.scatter_matrix(df, figsize=(10,10));

Create a graph displaying the distribution of those who survived

(Any appropriate graph works including pie chart or bar chart or other appropriate.)

#Your code here

Create a graph depicting the survival distribution by gender.

#Your code here

Create a graph depicting survival by age

#Your code here

Create a new colmn age bracket with the following categories:

• 0-12 years old
• 12-18 years old
• 18-25 years old
• 25-35 years old
• 35-45 years old
• 45-65 years old
• 65+ years old

#Your code here

Create a graph displaying the distribution of those who survived by the age brackets created above

#Your code here

Explore at least 3 other relationships within the data.

# Your code here

# Your code here

# Your code here