In [2]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt


    

In [7]:

    

time = np.arange(10)


    

In [8]:

    

time


    

    
Out[8]:





array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [15]:

    

position = np.arange(0, 100, 10)


    

In [16]:

    

position


    

    
Out[16]:





array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])

In [17]:

    

len(time)


    

    
Out[17]:





10

In [18]:

    

len(position)


    

    
Out[18]:





10

In [19]:

    

plt.plot(time, position)
plt.xlabel('Time (hr)')
plt.ylabel('Position (km)')


    

    
Out[19]:





Text(0,0.5,'Position (km)')






    

In [27]:

    

# implicit plot a dataframe
import pandas as pd

europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')

# extract just the year from columns
years = europe.columns.str.strip('gdpPercap_')

# convert years to integers and save it back to dataframe
europe.columns = years.astype(int)

europe.loc['Germany'].plot()


    

    
Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x116efd128>






    

In [29]:

    

europe_t = europe.T
europe_t.plot()
plt.ylabel('GDP per capita')


    

    
Out[29]:





Text(0,0.5,'GDP per capita')






    

In [ ]: