Editing the Date Formatting of X-Axis Tick Labels in Matplotlib

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

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)    

About python matplotlib x-axis date format setting

You need both a locator and formatter. Reference: https://matplotlib.org/stable/gallery/text_labels_and_annotations/date.html And the way you imported mdates isn't how it's usually done, so I've adjusted that too.

# ... other imports
import matplotlib.dates as mdates
 
startday = '2021-11-03' 
endday = '2021-11-12' 
ticker_list = ['2303.TW','2610.TW','2618.TW'] 
data = pd.DataFrame(columns=ticker_list) 
for ticker in ticker_list: 
    data[ticker] = yf.download(ticker, startday,endday)['Adj Close']
    ans=data.head()  
    data.plot(figsize=(8,12)) 
    plt.legend() 
    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei'] 
    plt.rcParams['axes.unicode_minus'] = False 
    plt.title("Line chart change of 7-day closing price of stock market", 
              fontsize=16) 
    plt.ylabel('closing price', fontsize=14) 
    plt.xlabel('Day', fontsize=14) 
    plt.grid(which="major", color='k', linestyle='-.', linewidth=0.5) 
    fmt_day = mdates.DayLocator() # provides a list of days 
    plt.gca().xaxis.set_major_locator(fmt_day) 
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d")) 
    plt.show()                                                        

How to format the x-axis to show every year on the major ticks

• When working with datetime data on the axis, matplotlib.dates should be used.
• Using the implementation shown for Date tick labels
• datemin and datemax must be in a datetime format to be recognized by the DateFormatter. As such, use np.datetime64(data.index.array[0], 'Y'), which results in numpy.datetime64('2021'), where as data.index.year.min() results in an int.

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

# create a sample dataframe
data = pd.DataFrame({'v1': [10]*4000, 'v2': [20]*4000}, index=pd.bdate_range('2021-01-11', freq='D', periods=4000))

years = mdates.YearLocator()   # every year
years_fmt = mdates.DateFormatter('%Y')

# create the plot
ax = data.plot.area(x='date', figsize=(8, 6))

# format the ticks only for years on the major ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)

# round to nearest years. Also, the index must be sorted and in a datetime format.
datemin = np.datetime64(data.index.array[0], 'Y')
datemax = np.datetime64(data.index.array[-1], 'Y') + np.timedelta64(1, 'Y')

# set the x-axis limits
ax.set_xlim(datemin, datemax)

# turn the grid on, if desired
ax.grid(True)

plt.show()

Formatted Plot

Unformatted Plot

ax = data.plot.area(figsize=(8, 6))

How to change the datetime tick label frequency for matplotlib plots

First of all you have to convert pandas date objects to python date objects. This conversion is needed because of matplotlib internal date conversion functions. Then use functions from matplotlib.dates to set desired formatter and tick positions like here:

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

# convert date objects from pandas format to python datetime
index = pd.date_range(start = "2015-07-01", end = "2017-01-01", freq = "D")
index = [pd.to_datetime(date, format='%Y-%m-%d').date() for date in index]
data = np.random.randint(1,100, size=len(index))
df = pd.DataFrame(data=data,index=index, columns=['data'])
print (df.head())

ax = df.plot()
# set monthly locator
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))
# set formatter
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
# set font and rotation for date tick labels
plt.gcf().autofmt_xdate()

plt.show()

For season labels you have to construct it by yourself and then set it with plt.setp function (for month 02 set label winter, 04 - spring etc.):
plt.setp(new_labels, rotation=90, fontsize=9).

head of df:

            data
2015-07-01    26
2015-07-02    33
2015-07-03    46
2015-07-04    69
2015-07-05    17

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