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
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
# ...