CODEWITHSUNDEEP
pythonmatplotlibseaborn
notIntoXS
notIntoXS

Reputation: 129

How to tell Seaborn/Matplotlib to show 'what you are given' on the X axis and do not assume I want the ticks to be evenly spaced numerically

I have a dataframe with one column that has YYYYMM data as a float, and a second column has summaraized data for the month.

enter image description here

I have tried to create another dataframe column but it was never going to work as the intended output is not a date.

pd.to_datetime(mthly_PL_withDate_df['YearMth'].astype(str), format='%Y%m')

So when I plot, sns/mpl creates X axis ticks that are numerically evenly spaced when my data 'YearMth' is not. How do I tell sns/mpl to "just show what you are given and don't try to 'niceify' it". I would have thought "showing what your are given without the introduction of assumptions" would be the default.

with sns.axes_style('whitegrid'):
g = sns.relplot(x='YearMth', y='PL', data=mthly_PL_withDate_df, height=5, aspect=1.5)

enter image description here

Upvotes: 0

Views: 335

Answers (1)

Simas Joneliunas
Simas Joneliunas

Reputation: 3118

You are using a float on the x-axis. It is normal for seaborn to assume that the data is numerical instead of categorical.

If you want to keep the dates as floats (strings are more correct in this case btw), you can easily fix that by setting the x_ticks property and passing your YearMth column to it as ax.set_xticks([0,2,4,6]).

This property, however, is not managed by seaborn, but instead by the underlying matplotlib.pyplot package. Thus you will need to modify your code a bit to expose the subplot object and later pass it to seaborn. 

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

data = [(10, 1), (11, 2), (12, 3), (1000, 4)]
data = pd.DataFrame.from_records(data=data, columns=['x', 'y'])

fig, ax = plt.subplots(figsize = (12,6))
ax.set_title("Plot with controlled ticks")
ax.set_ylabel("Mode data")
ax.set_xlabel("X values with good ticks")
ax.set_xticks(range(0,len(data['x'])))
ax.set_xticklabels(list(data['x']), rotation=45)


rel_plot = sns.relplot(ax=ax, data=data, height=5, aspect=1.5)

The code above will give you the expected result: enter image description here

Overall, subplots give you very fine control over the visualization of the displayed data (you can look up the full subplot api here).

Upvotes: 1

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