In [1]:

    

# Import all libraries needed for the tutorial

# General syntax to import specific functions in a library: 
##from (library) import (specific library function)
from pandas import DataFrame, read_csv, Series

# General syntax to import a library but no functions: 
##import (library) as (give the library a nickname/alias)
import matplotlib.pyplot as plt
import pandas as pd #this is how I usually import pandas
import sys #only needed to determine Python version number

# Enable inline plotting
%matplotlib inline


    

In [2]:

    

print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)


    

    




Python version 3.4.0 (default, Apr 11 2014, 13:05:11) 
[GCC 4.8.2]
Pandas version 0.16.2

In [3]:

    

# The inital set of baby names and bith rates
names = ['Bob','Jessica','Mary','John','Mel']
births = [968, 155, 77, 578, 973]


    

In [4]:

    

zip?


    

In [5]:

    

BabyDataSet = zip(names,births)
BabyDataSet = list(BabyDataSet)
BabyDataSet


    

    
Out[5]:





[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]

In [6]:

    

df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])
df


    

    
Out[6]:






  

    

      

      
Names
      
Births
    
  
  

    

      
0
      
Bob
      
968
    
    

      
1
      
Jessica
      
155
    
    

      
2
      
Mary
      
77
    
    

      
3
      
John
      
578
    
    

      
4
      
Mel
      
973
    
  

In [7]:

    

df.to_csv?


    

In [8]:

    

df.to_csv('births1880.csv',index=False,header=False)


    

In [9]:

    

read_csv?


    

In [10]:

    

# path of csv file
location = 'births1880.csv'
df = pd.read_csv(location)


    

In [11]:

    

df
# This brings us the our first problem of the exercise. The read_csv function treated the first
# record in the csv file as the header names. This is obviously not correct since the text file 
# did not provide us with header names.


    

    
Out[11]:






  

    

      

      
Bob
      
968
    
  
  

    

      
0
      
Jessica
      
155
    
    

      
1
      
Mary
      
77
    
    

      
2
      
John
      
578
    
    

      
3
      
Mel
      
973
    
  

In [12]:

    

# 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


    

    
Out[12]:






  

    

      

      
0
      
1
    
  
  

    

      
0
      
Bob
      
968
    
    

      
1
      
Jessica
      
155
    
    

      
2
      
Mary
      
77
    
    

      
3
      
John
      
578
    
    

      
4
      
Mel
      
973
    
  

In [13]:

    

# If we wanted to give the columns specific names, we would have to pass another paramter called names.
df = pd.read_csv(location, names=['Names','Births'])
df
# 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.


    

    
Out[13]:






  

    

      

      
Names
      
Births
    
  
  

    

      
0
      
Bob
      
968
    
    

      
1
      
Jessica
      
155
    
    

      
2
      
Mary
      
77
    
    

      
3
      
John
      
578
    
    

      
4
      
Mel
      
973
    
  

In [14]:

    

import os
os.remove(location)


    

In [15]:

    

# Check data type of the columns
df.dtypes


    

    
Out[15]:





Names     object
Births     int64
dtype: object

In [16]:

    

# Check data type of Births column
df.Births.dtype


    

    
Out[16]:





dtype('int64')

In [17]:

    

# Analyze Data
# Method 1:
Sorted = df.sort(['Births'], ascending=False)
Sorted.head(1)


    

    
Out[17]:






  

    

      

      
Names
      
Births
    
  
  

    

      
4
      
Mel
      
973
    
  

In [18]:

    

# Method 2:
df['Births'].max()


    

    
Out[18]:





973

In [20]:

    

# Present Data
# Create graph
df['Births'].plot()

# Maximum value in the data set
MaxValue = df['Births'].max()

# Name associated with the maximum value
MaxName = df['Names'][df['Births'] == df['Births'].max()].values

# Text to display on graph
Text = str(MaxValue) + " - " + MaxName

# Add text to graph
plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0), 
                 xycoords=('axes fraction', 'data'), textcoords='offset points')

print("The most popular name")
df[df['Births'] == df['Births'].max()]
#Sorted.head(1) can also be used


    

    




The most popular name






    
Out[20]:






  

    

      

      
Names
      
Births
    
  
  

    

      
4
      
Mel
      
973
    
  








    

In [ ]: