overruling data frame index when plotting with matplotlib

Question

I want to plot with data frames, but sometimes, I want more control over my x-tick labels and it looks like the data frame index is 'overruling' my code. here is the code:

test_df = pd.DataFrame({'cycles':[0,'b',3,'d','e','f','g'],'me':[100,80,99,100,75,100,90], 'you':[100,80,99,100,75,100,90], 'us':[100,80,99,100,75,100,90]})
f, ax = plt.subplots()
x = test_df['me']
x.index = ['a','b','c','d','e','f','g']



print(x)
for a in ax.get_xticklabels():
    a.set_text('me')

print(ax.get_xticklabels()[0])
ax.plot(x)
test_df.plot(x = 'cycles', y = 'me')

any idea on easier ways to easily modify x-tick labels for data frames easily without changing the index of the data frame, but easily just on the fly making the x-ticks whatever I want for any data frame column I want?

I.e. I want to set individual x-ticks, but I had to change the index in order to have the tick labels that I wanted, if I didn't, the index of the data frame would be the x-tick labels — squirrely
– squirrely, Commented Sep 11, 2018 at 16:02

ALollz · Accepted Answer · 2018-09-11 16:59:08Z

1

You can specify the xticks within DataFrame.plot. This is basically just a dummy to ensure the number of tick labels is correct.

Then just set the tick labels manually after the plot.

import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'cycles':[0,'b',3,'d','e','f','g'],
                   'me':[100,80,99,100,75,100,90]})

fig, ax = plt.subplots()
test_df.plot(x='cycles', y='me', ax=ax, xticks=test_df.index)
_ = ax.set_xticklabels(test_df['cycles'])
plt.show()

But you should be a bit hesitant of how the xticks aren't automatically generated. Line plots make sense when your values are ordinal. It doesn't seem obvious to me that 0 should be connected with 'b' anymore than 'e' should be connected to 'f'. In this situation a bar plot makes sense, and not-surprisingly, the xticks are generated without issue.

test_df.plot(x='cycles', y='me', kind='bar', legend=False)

edited Sep 11, 2018 at 16:59

answered Sep 11, 2018 at 16:51

ALollz

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