changing the xlim by date in matplotlib

import datetime
import matplotlib.pyplot as plt

x = [datetime.date(2014, 1, 29)] * 3
y = [2, 4, 1]

fig, ax = plt.subplots()
ax.plot_date(x, y, markerfacecolor = 'CornflowerBlue', markeredgecolor = 'white')
fig.autofmt_xdate()
ax.set_xlim([datetime.date(2014, 1, 26), datetime.date(2014, 2, 1)])
ax.set_ylim([0, 5])

import matplotlib.pyplot as plt
import datetime as dt

# Set X range.Using left and right variables makes it easy to change the range.
#
left = dt.date(2020, 3, 15)
right = dt.date(2020, 7, 15)

# Create scatter plot of Positive Cases
#
plt.scatter(
   x, y, c = "blue", edgecolor = "black",
   linewidths = 1, marker = "o", alpha = 0.8, label = "Total Positive Tested"
)

# Format the date into months & days
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))

# Change the tick interval
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval = 30))

# Puts x - axis labels on an angle
plt.gca().xaxis.set_tick_params(rotation = 30)

# Changes x - axis range
plt.gca().set_xbound(left, right)

plt.show()

# Import necessary packages
import os
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.dates
import DateFormatter
import seaborn as sns
import pandas as pd
import earthpy as et

# Handle date time conversions between pandas and matplotlib
from pandas.plotting
import register_matplotlib_converters
register_matplotlib_converters()

# Use white grid plot background from seaborn
sns.set(font_scale = 1.5, style = "whitegrid")

# Download the data
data = et.data.get_data('colorado-flood')

# Set working directory
os.chdir(os.path.join(et.io.HOME, 'earth-analytics'))

# Define relative path to file with daily precip
file_path = os.path.join("data", "colorado-flood",
   "precipitation",
   "805325-precip-dailysum-2003-2013.csv")

# Import data using datetime and no data value
precip_2003_2013_daily = pd.read_csv(file_path,
   parse_dates = ['DATE'],
   index_col = ['DATE'],
   na_values = ['999.99'])

# Subset data to June - Aug 2005
precip_june_aug_2005 = precip_2003_2013_daily['2005-06-01': '2005-08-31']

precip_june_aug_2005.head()

# Create figure and plot space
fig, ax = plt.subplots(figsize = (12, 12))

# Add x - axis and y - axis
ax.bar(precip_june_aug_2005.index.values,
   precip_june_aug_2005['DAILY_PRECIP'],
   color = 'purple')

# Set title and labels
for axes
ax.set(xlabel = "Date",
   ylabel = "Precipitation (inches)",
   title = "Daily Total Precipitation\nJune - Aug 2005 for Boulder Creek")

plt.show()

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook

# Load a numpy structured array from yahoo csv data with fields date, open,
# close, volume, adj_close from the mpl - data / example directory.This array
# stores the date as an np.datetime64 with a day unit('D') in the 'date'
# column.
data = cbook.get_sample_data('goog.npz', np_load = True)['price_data']

fig, axs = plt.subplots(3, 1, figsize = (6.4, 7), constrained_layout = True)
# common to all three:
   for ax in axs:
   ax.plot('date', 'adj_close', data = data)
# Major ticks every half year, minor ticks every month,
ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth = (1, 7)))
ax.xaxis.set_minor_locator(mdates.MonthLocator())
ax.grid(True)
ax.set_ylabel(r 'Price [\$]')

# different formats:
   ax = axs[0]
ax.set_title('DefaultFormatter', loc = 'left', y = 0.85, x = 0.02, fontsize = 'medium')

ax = axs[1]
ax.set_title('ConciseFormatter', loc = 'left', y = 0.85, x = 0.02, fontsize = 'medium')
ax.xaxis.set_major_formatter(
   mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))

ax = axs[2]
ax.set_title('Manual DateFormatter', loc = 'left', y = 0.85, x = 0.02,
   fontsize = 'medium')
# Text in the x axis will be displayed in 'YYYY-mm'
format.
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%b'))
# Rotates and right - aligns the x labels so they don 't crowd each other.
for label in ax.get_xticklabels(which = 'major'):
   label.set(rotation = 30, horizontalalignment = 'right')

plt.show()

XDates = [datetime(2021, 6, 1: 30) datetime(2021, 7, 1: 31)];
YNumsForXDates = sin(0: 0.1: 6);
plot(XDates, YNumsForXDates)

XTimes = seconds(0: 120);
YNumsForXTimes = cos(0: 0.05: 6);
plot(XTimes, YNumsForXTimes)

plot(XDates, YNumsForXDates)
xlim([datetime("2021-06-20") datetime("2021-07-07")])

xlim([datetime("2021-06-20") datetime("2021-06-22")])

plot(XTimes, YNumsForXTimes)
xticks(seconds([0 60 120]))

plot(XDates, YNumsForXDates)
xtickformat("yyyy-MM-dd")

 import datetime
 import matplotlib.pyplot as plt x = [datetime.date(2014, 1, 29), datetime.date(2014, 1, 29), datetime.date(2014, 1, 29)] y = [2, 4, 1] fig, ax = plt.subplots() ax.plot_date(x, y) ax.set_xlim([x[0], x[-1]]) canvas = FigureCanvas(plt.figure(1)) response = HttpResponse(content_type = 'image/png') canvas.print_png(response) return response

import datetime
import matplotlib.pyplot as plt x = [datetime.date(2014, 1, 29)] * 3 y = [2, 4, 1] fig, ax = plt.subplots() ax.plot_date(x, y, markerfacecolor = 'CornflowerBlue', markeredgecolor = 'white') fig.autofmt_xdate() ax.set_xlim([datetime.date(2014, 1, 26), datetime.date(2014, 2, 1)]) ax.set_ylim([0, 5])

import datetime
import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = [datetime.date(2021, 1, 1), datetime.date(2021, 1, 3), datetime.date(2021, 1, 5), datetime.date(2021, 1, 7)] y = [1, 3, 5, 7] fig, ax = plt.subplots() ax.plot_date(x, y, markerfacecolor = 'green', markeredgecolor = 'red', ms = 7) fig.autofmt_xdate() ax.set_xlim([datetime.date(2020, 12, 31), datetime.date(2021, 1, 8)]) ax.set_ylim([0, 8]) plt.show()