CODEWITHSUNDEEP
pythonpandasmatplotlibbar-chart
optimus_prime
optimus_prime

Reputation: 827

Plotting 3 different graphs from a multi indexed Pandas dataframe

Below is my multi indexed dataframe

    travel_spending
state   ethinicity  
CA  Asian   233.404580
MA  Asian   748.117647
NY  Asian   350.880000
CA  Black   146.898148
MA  Black   99.849057
NY  Black   125.206897
CA  Chinese 387.398601
MA  Chinese 119.636364
NY  Chinese 263.245283
CA  Hispanic    131.156484
MA  Hispanic    200.220859
NY  Hispanic    175.738589
CA  American Indian 36.500000
MA  American Indian 67.500000
NY  American Indian 81.800000
CA  Japanese    365.029703
MA  Japanese    28.666667
NY  Japanese    241.500000
CA  Other   257.953356
MA  Other   208.178174
NY  Other   255.144436
CA  Portuguese  26.000000
MA  Portuguese  22.000000
CA  White   222.322485
MA  White   167.293194
NY  White   140.080838

I am able to generate subplots from these very easily:

  travel_df_new.unstack(level=0).plot(kind='bar', subplots=True)

bar graphs

I want to get rid of the extra text that is inside the graph as it is redundant and also present outside it travel_spending,CA travel_spending MA,travel_spending, NY

ALso, I want to make the font of x axis(Ethinicity) more legible and make it more darker.

Are these things possible with matplotlib graphs?

Upvotes: 0

Views: 148

Answers (1)

Craig
Craig

Reputation: 4855

Removing the legend

You can get rid of the legend by passing legend=False as a parameter to the .plot() method of the dataframe:

travel_df_new.unstack(level=0).plot(kind='bar', subplots=True, legend=False)

Figure appearance

(Note: it looks to me like the OP might be using the ggplot style with matplotlib, but this isn't specified in the question. Based on this, I'm using the ggplot style for this example.)

import matplotlib
matplotlib.style.use('ggplot')

One option is to set the tick_params for the current axis immediately after the plot is created. You can retrieve the current axis with plt.gca(). Specify the appearance of the axis ticks by calling the tick_params() method for the axis object. The example below shows setting the labelsize and the labelcolor for the x-axis only.

import matplotlib.pyplot as plt
travel_df_new.unstack(level=0).plot(kind='bar', subplots=True, legend=False)
plt.gca().tick_params(axis='x', labelsize='x-large', labelcolor='k')

The method above will only apply to the current plot, but you can also customize the appearance of elements in all matplotlib figures by setting values in the rcParams dictionary. For example, this changes the color and size of the xticks:

import matplotlib.pyplot as plt
plt.rcParams['xtick.color']='k'
plt.rcParams['xtick.labelsize']='x-large'

travel_df_new.unstack(level=0).plot(kind='bar', subplots=True, legend=False)

These settings produce the following graph:

Easier to read x-ticks

Rotating the tick labels

It looks like the ability to set the labelrotation parameter will be added to tick_params() in a future version of matplotlib. It shows up as an option in the latest documentation, but it isn't available in version 2.0.

In current versions of matplotlib (<= 2.0.x), use plt.setp() to set the rotation.

travel_df_new.unstack(level=0).plot(kind='bar', subplots=True, legend=False)
plt.setp(plt.xticks()[-1], size='x-large', color='k', rotation=45, ha='right')

This call can also set the color and the size of the labels, so you can cover all of the tick formatting with this one call. The ha parameter controls the horizontal alignment of the rotated labels and accepts the values 'left', 'center', and 'right'.

Upvotes: 1

Related Questions