Panel data

So far, we have seen data taken from multiple individuals but at one point in time (cross-sectional) or taken from an individual entity but over multiple points in time (time series). However, if we observe multiple entities over multiple points in time we get a panel data also known as longitudinal data. Extending our earlier example about the military expenditure, let us now consider four countries over the same period of 1960-2010. The resulting data will be a panel dataset. The figure given below illustrates the panel data in this scenario. Rows with missing values, corresponding to the period 1960 to 1987 have been dropped before plotting the data.

Let us prepare the data that is required to plot the preceding figure. We will continue to expand the code we have used for the cross-sectional and time series data previously in this chapter. We start by creating a DataFrame having the data for the four companies mentioned in the preceding plot. This is done as follows:

chn = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\
               (data['Country Code']=='CHN'),index0:index1+1
             ]
chn = pd.Series(data=chn.values[0], index=chn.columns)
chn.dropna(inplace=True)

usa = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\
               (data['Country Code']=='USA'),index0:index1+1
             ]
usa = pd.Series(data=usa.values[0], index=usa.columns)
usa.dropna(inplace=True)

ind = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\
               (data['Country Code']=='IND'),index0:index1+1
             ]
ind = pd.Series(data=ind.values[0], index=ind.columns)
ind.dropna(inplace=True)

gbr = data.ix[(data['Indicator Name']=='Military expenditure (% of GDP)')&\
               (data['Country Code']=='GBR'),index0:index1+1
             ]
gbr = pd.Series(data=gbr.values[0], index=gbr.columns)
gbr.dropna(inplace=True)

Now that the data is ready for all five countries, we will plot them using the following code:

plt.figure(figsize=(5.5, 5.5))
usa.plot(linestyle='-', marker='*', color='b')
chn.plot(linestyle='-', marker='*', color='r')
gbr.plot(linestyle='-', marker='*', color='g')
ind.plot(linestyle='-', marker='*', color='y')
plt.legend(['USA','CHINA','UK','INDIA'], loc=1)
plt.title('Miltitary expenditure of 5 countries over 10 years')
plt.ylabel('Military expenditure (% of GDP)')
plt.xlabel('Years')s

The discussion about different types of data sets the stage for a closer look at time series. We will start doing that by understanding the special properties of data that can be typically found in a time series or panel data with inherent time series in it.