Changing the Formatting of a Datetime Axis in Matplotlib

Changing the formatting of a datetime axis in matplotlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# sample data
N = 30
drange = pd.date_range("2014-01", periods=N, freq="MS")
np.random.seed(365)  # for a reproducible example of values
values = {'values':np.random.randint(1,20,size=N)}
df = pd.DataFrame(values, index=drange)

fig, ax = plt.subplots()
ax.plot(df.index, df.values)
ax.set_xticks(df.index)

# use formatters to specify major and minor ticks
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
ax.xaxis.set_minor_formatter(mdates.DateFormatter("%Y-%m"))
_ = plt.xticks(rotation=90)    

Changing the formatting of a datetime axis in matplotlib (bar chart)

You can transform your time index to the appropriate format with strftime and then do the plot, passing rot=1 for the label rotation:

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

ts = pd.Series(np.random.randn(1000),index=pd.date_range('1/2/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000,4), index = ts.index, columns = list('ABCD'))
df = df.cumsum()

df.index = df.index.strftime('%Y-%m-%d')
df.iloc[0:5].plot.bar(rot=1)

Changing the formatting of a datetime.time axis in matplotlib

Matplotlib can handle datetime-objects easier than time objects. You can remove .time(). This code should work, I edited the dates to show changing x-values on the axis.

import matplotlib.dates as mdate
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

dates = ["15:05:35", "16:08:36", "17:09:37"]
# remove .time() from strptime
xs = [datetime.strptime(d, "%H:%M:%S") for d in dates]

ys = range(len(xs))

plt.gca().xaxis.set_major_formatter(mdate.DateFormatter("%H:%M"))
plt.gca().xaxis.set_major_locator(mdate.DayLocator())

# show all x-values on the x-axis
plt.xticks(xs)
# Plot
plt.plot(xs, ys)

plt.show()

Problem formatting datetime on matplotlib axis to get just hour and minutes

• This seems mostly the same as this answer to Date axis in heatmap seaborn
• Use .pivot to transform the dataframe, and then convert the columns 'H:M' format with .strftime('%H:%M')
• Use xticklabels=1 and yticklabels=1 in seaborn.heatmap to show all the values.
• The ticks are 0 indexed and discrete, not datetime indexed. The value shown is just the label. See p.get_xticklabels()

# pivot daux
dfp = daux.pivot(index='dayofweek', columns='resolved_at', values='n_pkgs')

# convert the columns to H:M
dfp.columns = dfp.columns.strftime('%H:%M')

# plot
fig = plt.figure(figsize=(12, 6))
p = sns.heatmap(dfp, xticklabels=1, yticklabels=1)

Editing the date formatting of x-axis tick labels

In short:

import matplotlib.dates as mdates
myFmt = mdates.DateFormatter('%d')
ax.xaxis.set_major_formatter(myFmt)

Many examples on the matplotlib website. The one I most commonly use is here

Date formatting on x-axis in matplotlib

Due to the way seaborn handles the trailing zeros, think this is not getting set properly.
One way to fix it is to use strftime() AFTER you have sorted the data to change the datetime back to string like this...

maindf['Date'] =  pd.to_datetime(maindf['Date'])
maindf.sort_values('Date', inplace=True)
maindf['Date'] = maindf['Date'].dt.strftime('%d-%m-%Y') ## Change to format you need
sns.boxplot(data=maindf, x='Date', y='Chemical', hue='Group')
plt.xticks(rotation=40)
plt.show()

The other option is to use get & set xticklabels() by removing the everything from T onwards, so that you see the right format.

maindf['Date'] =  pd.to_datetime(maindf['Date'])
maindf.sort_values('Date', inplace=True)
sns.boxplot(data=maindf, x='Date', y='Chemical', hue='Group')
plt.gca().set_xticklabels([date_text.get_text().split("T")[0] for date_text in plt.gca().get_xticklabels()])
plt.xticks(rotation=40)
plt.show()

Both would give you the below date format...

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