Ordering and Formatting Dates on X-Axis in Seaborn Bar Plot

Question

This seems so simple, but for the life of me I can't figure it out.

I am new to Python and Seaborn, and I am doing all this online at PythonAnywhere.

All I am trying to do is create a simple barplot in seaborn, with dates ordered properly (that is, ascending from left to right), on the x-axis.

When I try this:

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
import pandas as pd
import seaborn as sns

emp = pd.DataFrame([[32, "5/31/2018"], [3, "2/28/2018"], [40, "11/30/2017"], [50, "8/31/2017"], [51, "5/31/2017"]], 
               columns=["jobs", "12monthsEnding"])

fig = plt.figure(figsize = (10,7))

sns.barplot(x = "12monthsEnding", y = "uniqueClientExits", data = emp, 
estimator = sum, ci = None)

fig.autofmt_xdate()
plt.show()

I get this:

Nice looking bar graph but with the dates ordered descending from left to right

And then when I try to convert the objects to datetime:

(note: i'm using pd.to_datetime() below in order to try and recreate what happens when I use parse_dates in pd.read_csv(), which is how I'm actually creating the dataframe.)

emp = pd.DataFrame([[32, pd.to_datetime("5/31/2018")], [3, pd.to_datetime("2/28/2018")], [40, pd.to_datetime("11/30/2017")], [50, pd.to_datetime("8/31/2017")], [51, pd.to_datetime("5/31/2017")]], 
               columns=["jobs", "12monthsEnding"])

fig = plt.figure(figsize = (10,7))

sns.barplot(x = "12monthsEnding", y = "uniqueClientExits", data = emp, 
estimator = sum, ci = None)

fig.autofmt_xdate()

plt.show()

I get this:

Bar plot with the dates in the right order, but WRONG format

I get the same bar plot, with the dates ordered properly, but in the full, long datetime format, with the time, etc. But all I want is the day/month/year.

I've scoured stackoverflow for two days now and nothing has worked. I'm starting to wonder if part of the reason is because I'm working on PythonAnywhere. But I also can't find any reason why that would be.

This is driving me nuts. Looking forward to any assistance. Thanks.

Answer 1

Using your second approach, simply sort and reformat the datetime values to YYYY-MM-DD and pass values into set_xticklabels. Below demonstrates with random, seeded data:

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

# RANDOM DATA
np.random.seed(62918)
emp = pd.DataFrame({'uniqueClientExits': [np.random.randint(15) for _ in range(50)],
                    '12monthsEnding': pd.to_datetime(
                                          np.random.choice(
                                              pd.date_range('2018-01-01', periods=50), 
                                          50)
                                      )
                   }, columns = ['uniqueClientExits','12monthsEnding'])

# PLOTTING
fig, ax = plt.subplots(figsize = (12,6))    
fig = sns.barplot(x = "12monthsEnding", y = "uniqueClientExits", data = emp, 
                  estimator = sum, ci = None, ax=ax)

x_dates = emp['12monthsEnding'].dt.strftime('%Y-%m-%d').sort_values().unique()
ax.set_xticklabels(labels=x_dates, rotation=45, ha='right')

To check graph output, run a groupby().sum():

print(emp.groupby('12monthsEnding').sum().head())

#                 uniqueClientExits
# 12monthsEnding                   
# 2018-01-01                     12
# 2018-01-02                      4
# 2018-01-04                     11
# 2018-01-06                     13
# 2018-01-08                     10
# 2018-01-11                     11
# 2018-01-14                      9
# 2018-01-15                      0
# 2018-01-16                      4
# 2018-01-17                      5
# ...