In [1]:

    

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

%matplotlib inline


    

In [41]:

    

df = pd.DataFrame({
    'name':['john','mary','peter','jeff','bill','lisa','jose'],
    'age':[23,78,22,19,45,33,20],
    'gender':['M','F','M','M','M','F','M'],
    'state':['california','dc','california','dc','california','texas','texas'],
    'num_children':[2,0,0,3,2,1,4],
    'num_pets':[5,1,0,5,2,2,3]
})
df[['name','age','gender','state','num_children','num_pets']]


    

    
Out[41]:







  

    

      

      
name
      
age
      
gender
      
state
      
num_children
      
num_pets
    
  
  

    

      
0
      
john
      
23
      
M
      
california
      
2
      
5
    
    

      
1
      
mary
      
78
      
F
      
dc
      
0
      
1
    
    

      
2
      
peter
      
22
      
M
      
california
      
0
      
0
    
    

      
3
      
jeff
      
19
      
M
      
dc
      
3
      
5
    
    

      
4
      
bill
      
45
      
M
      
california
      
2
      
2
    
    

      
5
      
lisa
      
33
      
F
      
texas
      
1
      
2
    
    

      
6
      
jose
      
20
      
M
      
texas
      
4
      
3
    
  

In [42]:

    

df.plot(kind='scatter',x='num_children',y='num_pets',color='red')


    

    
Out[42]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f0221647390>






    

In [43]:

    

df.plot(kind='bar',x='name',y='age')


    

    
Out[43]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f0221689860>






    

In [44]:

    

plt.clf()
# gca stands for 'get current axis'
ax = plt.gca()

df.plot(kind='line',x='name',y='num_children',ax=ax)
df.plot(kind='line',x='name',y='num_pets', color='red', ax=ax)

plt.show()


    

In [45]:

    

df.groupby('state').size().plot(kind='bar')


    

    
Out[45]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f0221500ef0>






    

In [46]:

    

df.groupby(['state','gender']).size().unstack().plot(kind='bar',stacked=True)
plt.show()


    

In [47]:

    

plt.clf()
df.groupby(['gender','state']).size().unstack().plot(kind='bar',stacked=True)
plt.legend(loc='lower right')
plt.gcf().set_size_inches(7,4)
plt.show()


    

    






<Figure size 432x288 with 0 Axes>






    

In [48]:

    

df[['age']].plot(kind='hist',bins=[0,20,40,60,80,100],rwidth=0.9)


    

    
Out[48]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f022152df28>






    

In [53]:

    

import matplotlib.pyplot as plt

# create dummy variable them group by that
# set the legend to false because we'll fix it later
df.assign(
 dummy = 1   
).groupby(['dummy','state']).size().to_frame().unstack().plot(kind='bar',stacked=True,legend=False)

plt.title('Number of records by State')

# other it'll show up as 'dummy' 
plt.xlabel('state')

# disable ticks in the x axis
plt.xticks([])

# fix the legend
current_handles, _ = plt.gca().get_legend_handles_labels()
reversed_handles = reversed(current_handles)

labels = reversed(df['state'].unique())

plt.legend(reversed_handles,labels,loc='lower right')

plt.gcf().set_size_inches(7,4)
plt.show()


    

In [11]:

    

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

# create dummy variable them group by that
# set the legend to false because we'll fix it later
df.assign(
 dummy = 1   
).groupby(['dummy','state']).size().groupby(level=0).apply(
    lambda x: 100 * x / x.sum()
).to_frame().unstack().plot(kind='bar',stacked=True,legend=False)


plt.title('Amount of records by State, normalized')


# other it'll show up as 'dummy' 
plt.xlabel('state')

# disable ticks in the x axis
plt.xticks([])

# fix the legend
current_handles, _ = plt.gca().get_legend_handles_labels()
reversed_handles = reversed(current_handles)

labels = reversed(df['state'].unique())

plt.legend(reversed_handles,labels,loc='lower right')

plt.gcf().set_size_inches(7,4)
plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.show()


    

In [12]:

    

import matplotlib.ticker as mtick

df.groupby(['gender','state']).size().groupby(level=0).apply(
    lambda x: 100 * x / x.sum()
).unstack().plot(kind='bar',stacked=True,legend='reverse')

plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.title('Amount of records by Gender and State, normalized')
# plt.legend(loc='lower right')
plt.gcf().set_size_inches(7,4)
plt.show()


    

In [13]:

    

df = pd.DataFrame({
    'name':['john','lisa','peter','carl','linda','betty'],
    'date_of_birth':[
        '01/21/1988','03/10/1977','07/25/1999','01/22/1977','09/30/1968','09/15/1970'
    ]
})

df


    

    
Out[13]:







  

    

      

      
name
      
date_of_birth
    
  
  

    

      
0
      
john
      
01/21/1988
    
    

      
1
      
lisa
      
03/10/1977
    
    

      
2
      
peter
      
07/25/1999
    
    

      
3
      
carl
      
01/22/1977
    
    

      
4
      
linda
      
09/30/1968
    
    

      
5
      
betty
      
09/15/1970
    
  

In [14]:

    

df['date_of_birth'] = pd.to_datetime(df['date_of_birth'],infer_datetime_format=True)


    

In [15]:

    

df.dtypes


    

    
Out[15]:





name                     object
date_of_birth    datetime64[ns]
dtype: object

In [16]:

    

plt.clf()
df['date_of_birth'].map(lambda d: d.month).plot(kind='hist')
plt.xlabel('Month number')
plt.show()


    

In [26]:

    

import matplotlib.pyplot as plt

# create dummy variable them group by that
# set the legend to false because we'll fix it later
df6.assign(
 dummy = 1   
).groupby(['dummy','state']).size().to_frame().unstack().plot(kind='bar',stacked=True,legend=False)


plt.title('Number of records by State')

# other it'll show up as 'dummy' 
plt.xlabel('state')

# disable ticks in the x axis
plt.xticks([])

# fix the legend
current_handles, _ = plt.gca().get_legend_handles_labels()
reversed_handles = reversed(current_handles)

labels = reversed(df6['state'].unique())

plt.legend(reversed_handles,labels,loc='lower right')

plt.gcf().set_size_inches(7,4)
plt.show()