How to create a pandas sub-headline timeline?

Question

I have a data frame like this:

value identifier 2007-01-01 0.781611 55 2007-01-01 0.766152 56 2007-01-01 0.766152 57 2007-02-01 0.705615 55 2007-02-01 0.032134 56 2007-02-01 0.032134 57 2008-01-01 0.026512 55 2008-01-01 0.993124 56 2008-01-01 0.993124 57 2008-02-01 0.226420 55 2008-02-01 0.033860 56 2008-02-01 0.033860 57 

So, I am doing groupby for id:

 df.groupby('identifier') 

And now I want to generate subnets in the grid, one graph for each group. I tried both

 df.groupby('identifier').plot(subplots=True) 

or

 df.groupby('identifier').plot(subplots=False) 

and

 plt.subplots(3,3) df.groupby('identifier').plot(subplots=True) 

to no avail. How to create graphs?

Answer 1

Here, an automatic layout with lots of groups (random fake data) and a game with grouped.get_group(key) will show you how to make more elegant plots.

 import pandas as pd from numpy.random import randint import matplotlib.pyplot as plt df = pd.DataFrame(randint(0,10,(200,6)),columns=list('abcdef')) grouped = df.groupby('a') rowlength = grouped.ngroups/2 # fix up if odd number of groups fig, axs = plt.subplots(figsize=(9,4), nrows=2, ncols=rowlength, # fix as above gridspec_kw=dict(hspace=0.4)) # Much control of gridspec targets = zip(grouped.groups.keys(), axs.flatten()) for i, (key, ax) in enumerate(targets): ax.plot(grouped.get_group(key)) ax.set_title('a=%d'%key) ax.legend() plt.show() 

Answer 2

You use pivot to get identifiers in columns and then draw

 pd.pivot_table(df.reset_index(), index='index', columns='identifier', values='value' ).plot(subplots=True) 

And, the conclusion

 pd.pivot_table(df.reset_index(), index='index', columns='identifier', values='value' ) 

Looks like -

 identifier 55 56 57 index 2007-01-01 0.781611 0.766152 0.766152 2007-02-01 0.705615 0.032134 0.032134 2008-01-01 0.026512 0.993124 0.993124 2008-02-01 0.226420 0.033860 0.033860 

Answer 3

If you have a multi-index series. Here is another solution for the wanted graph.

 df.unstack('indentifier').plot.line(subplots=True) 

Answer 4

Here is a solution for those who need to build graphs to study different levels of aggregation across groups across multiple columns.

 from numpy.random import randint from numpy.random import randint import matplotlib.pyplot as plt import numpy as np levels_bool = np.tile(np.arange(0,2), 100) levels_groups = np.repeat(np.arange(0,4), 50) x_axis = np.tile(np.arange(0,10), 20) values = randint(0,10,200) stacked = np.stack((levels_bool, levels_groups, x_axis, values), axis=0) df = pd.DataFrame(stacked.T, columns=['bool', 'groups', 'x_axis', 'values']) columns = len(df['bool'].unique()) rows = len(df['groups'].unique()) fig, axs = plt.subplots(rows, columns, figsize = (20,20)) y_index_counter = count(0) groupped_df = df.groupby([ 'groups', 'bool','x_axis']).agg({ 'values': ['min', 'mean', 'median', 'max'] }) for group_name, grp in groupped_df.groupby(['groups']): y_index = next(y_index_counter) x_index_counter = count(0) for boolean, grp2 in grp.groupby(['bool']): x_index = next(x_index_counter) axs[y_index, x_index].plot(grp2.reset_index()['x_axis'], grp2.reset_index()['values'], label=str(key)+str(key2)) axs[y_index, x_index].set_title("Group:{} Bool:{}".format(group_name, boolean)) ax.legend() plt.subplots_adjust(hspace=0.5) plt.show()