Create Data - We begin by creating our own data set for analysis. This prevents the end user reading this tutorial from having to download any files to replicate the results below. We will export this data set to a text file so that you can get some experience pulling data from a text file.
Get Data - We will learn how to read in the text file containing the baby names. The data consist of baby names born in the year 1880.
Prepare Data - Here we will simply take a look at the data and make sure it is clean. By clean I mean we will take a look inside the contents of the text file and look for any anomalities. These can include missing data, inconsistencies in the data, or any other data that seems out of place. If any are found we will then have to make decisions on what to do with these records.
Analyze Data - We will simply find the most popular name in a specific year.
Present Data - Through tabular data and a graph, clearly show the end user what is the most popular name in a specific year.
Make sure you have looked through all previous lessons as the knowledge learned in previous lessons will be needed for this exercise.
Numpy will be used to help generate the sample data set. Importing the libraries is the first step we will take in the lesson.
# Import all libraries needed for the tutorial import pandas as pd from numpy import random import matplotlib.pyplot as plt import sys #only needed to determine Python version number # Enable inline plotting %matplotlib inline
print 'Python version ' + sys.version print 'Pandas version ' + pd.__version__
Python version 2.7.5 |Anaconda 2.0.1 (64-bit)| (default, Jul 1 2013, 12:37:52) [MSC v.1500 64 bit (AMD64)] Pandas version 0.14.1
The data set will consist of 1,000 baby names and the number of births recorded for that year (1880). We will also add plenty of duplicates so you will see the same baby name more than once. You can think of the multiple entries per name simply being different hospitals around the country reporting the number of births per baby name. So if two hospitals reported the baby name "Bob", the data will have two values for the name Bob. We will start by creating the random set of baby names.
# The inital set of baby names names = ['Bob','Jessica','Mary','John','Mel']
To make a random list of 1,000 baby names using the five above we will do the following:
To do this we will be using the functions seed, randint, len, range, and zip.
# This will ensure the random samples below can be reproduced. # This means the random samples will always be identical. random.seed?
seed(500) - Create seed
randint(low=0,high=len(names)) - Generate a random integer between zero and the length of the list "names".
names[n] - Select the name where its index is equal to n.
for i in range(n) - Loop until i is equal to n, i.e. 1,2,3,....n.
random_names = Select a random name from the name list and do this n times.
random.seed(500) random_names = [names[random.randint(low=0,high=len(names))] for i in range(1000)] # Print first 10 records random_names[:10]
['Mary', 'Jessica', 'Jessica', 'Bob', 'Jessica', 'Jessica', 'Jessica', 'Mary', 'Mary', 'Mary']
Generate a random numbers between 0 and 1000
# The number of births per name for the year 1880 births = [random.randint(low=0,high=1000) for i in range(1000)] births[:10]
[968, 155, 77, 578, 973, 124, 155, 403, 199, 191]
Merge the names and the births data set using the zip function.
BabyDataSet = zip(random_names,births) BabyDataSet[:10]
[('Mary', 968), ('Jessica', 155), ('Jessica', 77), ('Bob', 578), ('Jessica', 973), ('Jessica', 124), ('Jessica', 155), ('Mary', 403), ('Mary', 199), ('Mary', 191)]
We are basically done creating the data set. We now will use the pandas library to export this data set into a csv file.
df will be a DataFrame object. You can think of this object holding the contents of the BabyDataSet in a format similar to a sql table or an excel spreadsheet. Lets take a look below at the contents inside df.
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births']) df[:10]
The only parameters we will use is index and header. Setting these parameters to False will prevent the index and header names from being exported. Change the values of these parameters to get a better understanding of their use.
To pull in the text file, we will use the pandas function read_csv. Let us take a look at this function and what inputs it takes.
Even though this functions has many parameters, we will simply pass it the location of the text file.
Location = C:\Users\TYPE_USER_NAME.xy\startups\births1880.txt
Note: Depending on where you save your notebooks, you may need to modify the location above.
Location = r'C:\Users\david\notebooks\pandas\births1880.txt' df = pd.read_csv(Location)
Notice the r before the string. Since the slashes are special characters, prefixing the string with a r will escape the whole string.
<class 'pandas.core.frame.DataFrame'> Int64Index: 999 entries, 0 to 998 Data columns (total 2 columns): Mary 999 non-null object 968 999 non-null int64 dtypes: int64(1), object(1)
To actually see the contents of the dataframe we can use the head() function which by default will return the first five records. You can also pass in a number n to return the top n records of the dataframe.
This brings us the our first problem of the exercise. The read_csv function treated the first record in the text file as the header names. This is obviously not correct since the text file did not provide us with header names.
To correct this we will pass the header parameter to the read_csv function and set it to None (means null in python).
df = pd.read_csv(Location, header=None) df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 0 to 999 Data columns (total 2 columns): 0 1000 non-null object 1 1000 non-null int64 dtypes: int64(1), object(1)
Summary now says:
Now lets take a look at the last five records of the dataframe
If we wanted to give the columns specific names, we would have to pass another paramter called names. We can also omit the header parameter.
df = pd.read_csv(Location, names=['Names','Births']) df.head(5)
You can think of the numbers [0,1,2,3,4,...] as the row numbers in an Excel file. In pandas these are part of the index of the dataframe. You can think of the index as the primary key of a sql table with the exception that an index is allowed to have duplicates.
[Names, Births] can be though of as column headers similar to the ones found in an Excel spreadsheet or sql database.
Delete the txt file now that we are done using it.
import os os.remove(Location)
The data we have consists of baby names and the number of births in the year 1880. We already know that we have 1,000 records and none of the records are missing (non-null values). We can verify the "Names" column still only has five unique names.
We can use the unique property of the dataframe to find all the unique records of the "Names" column.
# Method 1: df['Names'].unique()
array(['Mary', 'Jessica', 'Bob', 'John', 'Mel'], dtype=object)
# If you actually want to print the unique values: for x in df['Names'].unique(): print x
Mary Jessica Bob John Mel
# Method 2: print df['Names'].describe()
count 1000 unique 5 top Bob freq 206 dtype: object
Since we have multiple values per baby name, we need to aggregate this data so we only have a baby name appear once. This means the 1,000 rows will need to become 5. We can accomplish this by using the groupby function.
# Create a groupby object name = df.groupby('Names') # Apply the sum function to the groupby object df = name.sum() df
To find the most popular name or the baby name with the higest birth rate, we can do one of the following.
# Method 1: Sorted = df.sort(['Births'], ascending=False) Sorted.head(1)
# Method 2: df['Births'].max()
Here we can plot the Births column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that Bob is the most popular baby name in the data set.
# Create graph df['Births'].plot(kind='bar') print "The most popular name" df.sort(columns='Births', ascending=False)
The most popular name