01: Building a pandas Cheat Sheet, Part 1

Use the csv I've attached to answer the following questions

Import pandas with the right name
Set all graphics from matplotlib to display inline
Read the csv in (it should be UTF-8 already so you don't have to worry about encoding), save it with the proper boring name
Display the names of the columns in the csv
Display the first 3 animals.
Sort the animals to see the 3 longest animals.
What are the counts of the different values of the "animal" column? a.k.a. how many cats and how many dogs.
Only select the dogs.
Display all of the animals that are greater than 40 cm.
'length' is the animal's length in cm. Create a new column called inches that is the length in inches.
Save the cats to a separate variable called "cats." Save the dogs to a separate variable called "dogs."
Display all of the animals that are cats and above 12 inches long. First do it using the "cats" variable, then do it using your normal dataframe.
What's the mean length of a cat?
What's the mean length of a dog?
Use groupby to accomplish both of the above tasks at once.
Make a histogram of the length of dogs. I apologize that it is so boring.
Change your graphing style to be something else (anything else!)
Make a horizontal bar graph of the length of the animals, with their name as the label (look at the billionaires notebook I put on Slack!)
Make a sorted horizontal bar graph of the cats, with the larger cats on top.



In [2]:

    
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline









    



/usr/local/lib/python3.5/dist-packages/matplotlib/backends/backend_gtk3agg.py:18: UserWarning: The Gtk3Agg backend is known to not work on Python 3.x with pycairo. Try installing cairocffi.
  "The Gtk3Agg backend is known to not work on Python 3.x with pycairo. "



In [3]:

    
df=pd.read_csv("/home/sean/Downloads/07-hw-animals.csv")



In [4]:

    
print(df.columns.values)









    



['animal' 'name' 'length']



In [5]:

    
df.sort_values(by='length', ascending=False).head(3)









    Out[5]:






  
    
      
      animal
      name
      length
    
  
  
    
      2
      dog
      Egglesburg
      65
    
    
      3
      dog
      Devon
      50
    
    
      1
      cat
      Bob
      45



In [6]:

    
df['animal'].value_counts()









    Out[6]:





dog    3
cat    3
Name: animal, dtype: int64



In [7]:

    
df['animal']=='dog'
#this only gives a list of true/false values









    Out[7]:





0    False
1    False
2     True
3     True
4    False
5     True
Name: animal, dtype: bool



In [8]:

    
dogs=df[df['animal']=='dog']
#this will give a datafram



In [9]:

    
dogs









    Out[9]:






  
    
      
      animal
      name
      length
    
  
  
    
      2
      dog
      Egglesburg
      65
    
    
      3
      dog
      Devon
      50
    
    
      5
      dog
      Fontaine
      35



In [10]:

    
df['inches']=df['length']/2.54
df



In [11]:

    
cats=df[df['animal']=='cat']



In [12]:

    
cats[cats['inches']>12]



In [18]:

    
df[(df['animal']=='cat') & (df['inches']>12)]



In [19]:

    
df[df['animal']=='dog'].mean()









    Out[19]:





length    50.000000
inches    19.685039
dtype: float64



In [20]:

    
df[df['animal']=='cat'].mean()









    Out[20]:





length    37.333333
inches    14.698163
dtype: float64



In [23]:

    
df.groupby('animal').describe()



In [21]:

    
df.groupby('animal').mean()



In [24]:

    
dogs=df[df['animal']=='dog']



In [25]:

    
dogs['length'].hist()









    Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7c439ca470>



In [26]:

    
df['length'].plot(kind='bar')









    Out[26]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7c438d4da0>



In [27]:

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









    Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7c4182b198>



In [29]:

    
df.plot(kind='barh', x='name', y='length', legend=False)









    Out[29]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7c4171e668>



In [33]:

    
df[df['animal']=='cat'].sort_values(by='length').plot(kind='barh', x='name', y='length')









    Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f7c413dec88>



In [ ]:

		inches	length
animal
cat	count	3.000000	3.000000
	mean	14.698163	37.333333
	std	2.679866	6.806859
	min	12.598425	32.000000
	25%	13.188976	33.500000
	50%	13.779528	35.000000
	75%	15.748031	40.000000
	max	17.716535	45.000000
dog	count	3.000000	3.000000
	mean	19.685039	50.000000
	std	5.905512	15.000000
	min	13.779528	35.000000
	25%	16.732283	42.500000
	50%	19.685039	50.000000
	75%	22.637795	57.500000
	max	25.590551	65.000000

	animal	name	length	inches
0	cat	Anne	35	13.779528
1	cat	Bob	45	17.716535
2	dog	Egglesburg	65	25.590551
3	dog	Devon	50	19.685039
4	cat	Charlie	32	12.598425
5	dog	Fontaine	35	13.779528