Add summary statistics to your time series plot
It is possible to visualize time series plots and numerical summaries on one single graph by using the pandas
API to matplotlib
along with the table
method:
# Plot the time series data in the DataFrame
ax = df.plot()
# Compute summary statistics of the df DataFrame
df_summary = df.describe()
# Add summary table information to the plot
ax.table(cellText=df_summary.values,
colWidths=[0.3]*len(df.columns),
rowLabels=df_summary.index,
colLabels=df_summary.columns,
loc='top')
This is a part of the course
“Visualizing Time Series Data in Python”
Exercise instructions
Review meat_mean
in the shell -- a DataFrame that contains the mean of all the time series in meat
.
- Assign all the values in
meat_mean
to thecellText
argument. - Assign all the values in index of
meat_mean
to therowLabels
argument. - Assign the column names of
meat_mean
to thecolLabels
argument.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Plot the meat data
ax = meat.plot(fontsize=6, linewidth=1)
# Add x-axis labels
ax.set_xlabel('Date', fontsize=6)
# Add summary table information to the plot
ax.table(cellText=meat_mean.____,
colWidths = [0.15]*len(meat_mean.columns),
rowLabels=meat_mean.____,
colLabels=meat_mean.____,
loc='top')
# Specify the fontsize and location of your legend
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 0.95), ncol=3, fontsize=6)
# Show plot
plt.show()