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Reproduce simple pandas plot

I have a situation with my data. I like the behaviour of .plot() over a data frame. But sometimes it doesn't work, because the frequency of the time index is not an integer.
But reproducing the plot in matplotlib is OK. Just ugly.
The part that bother me the most is the settings of the x axis. The tick frequency and the limits. Is there any easy way that I can reproduce this behaviour in matplotlib?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Create Data
f = lambda x: np.sin(0.1*x) + 0.1*np.random.randn(1,x.shape[0])
x = np.arange(0,217,0.001)
y = f(x)
# Create DataFrame
data = pd.DataFrame(y.transpose(), columns=['dp'], index=None)
data['t'] = pd.date_range('2021-01-01 14:32:09', periods=len(data['dp']),freq='ms')
data.set_index('t', inplace=True)
# Pandas plot()
data.plot()
# Matplotlib plot (ugly x-axis)
plt.plot(data.index,data['dp'])
EDIT: Basically, what I want to achieve is a similar spacing in the xtics labels, and the tight margin adjust of the values. Legends and axis title, I can do them
Pandas output
Matplotlib output
Thanks

You can use some matplotlib date utilities:
Figure.autofmt_xdate() to unrotate and center the date labels
Axis.set_major_locator() to change the interval to 1 min
Axis.set_major_formatter() to reformat as %H:%M
fig, ax = plt.subplots()
ax.plot(data.index, data['dp'])
import matplotlib.dates as mdates
fig.autofmt_xdate(rotation=0, ha='center')
ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
# uncomment to remove the first `xtick`
# ax.set_xticks(ax.get_xticks()[1:])

Why am I getting junk date values on x-axis in matplotlib?

I am new to Python and learning data visualization using matplotlib.
I am trying to plot Date/Time vs Values using matplotlib from this CSV file:
https://drive.google.com/file/d/1ex2sElpsXhxfKXA4ZbFk30aBrmb6-Y3I/view?usp=sharing
Following is the code snippet which I have been playing around with:
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
plt.style.use('seaborn')
years = mdates.YearLocator()
months = mdates.MonthLocator()
days = mdates.DayLocator()
hours = mdates.HourLocator()
minutes = mdates.MinuteLocator()
years_fmt = mdates.DateFormatter('%H:%M')
data = pd.read_csv('datafile.csv')
data.sort_values('Date/Time', inplace=True)
fig, ax = plt.subplots()
ax.plot('Date/Time', 'Discharge', data=data)
# format the ticks
ax.xaxis.set_major_locator(minutes)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(hours)
datemin = min(data['Date/Time'])
datemax = max(data['Date/Time'])
ax.set_xlim(datemin, datemax)
ax.format_xdata = mdates.DateFormatter('%Y.%m.%d %H:%M')
ax.format_ydata = lambda x: '%1.2f' % x # format the price.
ax.grid(True)
fig.autofmt_xdate()
plt.show()
The code is plotting the graph but it is not labeling the X-Axis and also giving some unknown values (on mouse over) for x on the bottom right corner as shown in the below screenshot:
Screenshot of matplotlib figure window
Can someone please suggest what changes are needed to plot the x-axis dates and also make the correct values appear when I move the cursor over the graph?
Thanks

I haven't used matplotlib. Instead I used pandas plotting
import pandas as pd
data = pd.read_csv('datafile.csv')
data.sort_values('Date/Time', inplace=True)
data["Date/Time"] = pd.to_datetime(data["Date/Time"], format="%d.%m.%Y %H:%M")
ax = data.plot.line(x='Date/Time', y='Discharge')
Here, you need to convert the Date/Time to pandas datetime type.

The main issue you have there is that the date formats are mixed up - your data uses '%d.%m.%Y %H:%M', but you set '%Y.%m.%d %H:%M' and this is why you saw 'rubbish' values in x ticks labels. Anyway the number of lines in your code can be reduced heavily if you convert your Date/Time column to timestamps, ie.:
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
plt.style.use('seaborn')
data = pd.read_csv('datafile.csv')
data.sort_values('Date/Time', inplace=True)
data["Date/Time"] = pd.to_datetime(data["Date/Time"], format="%d.%m.%Y %H:%M")
data.sort_values('Date/Time', inplace=True)
fig, ax = plt.subplots()
ax.plot('Date/Time', 'Discharge', data=data)
ax.format_xdata = mdates.DateFormatter('%Y.%m.%d %H:%M')
ax.tick_params(axis='x', rotation=45)
ax.grid(True)
fig.autofmt_xdate()
plt.show()
Note that the format of labels in the plot will depend on the zoom level, so you will need to enlarge a portion of the graph to see hours and minutes in the tick labels, but the cursor locator on the bottom bar of the window should be always displaying the detailed timestamp under the cursor.

Measurement length for X and Y-axis

I wonder if it's possible to change the measurement milestones for graphs created by pandas. In my code the X-axis stands for time and is measured by month, but the measurement milestones are all over the place.
In the image below, the milestones for the X-axis are 2012M01, 2012M06, 2012M11, 2013M04 and 2013M09.
Is there any way I can choose how long the distance should be between every milestone? For example, to make it so it shows every year or every half year?
This is the code I used for the function making the graph:
def graph(dataframe):
graph = dataframe[["Profit"]].plot()
graph.set_title('Statistics')
graph.set_ylabel('Thousand $')
graph.set_xlabel('Time')
plt.grid(True)
plt.show()
The actual dataframe is just an excel-file with a bunch of months and monetary values in it.

I think the most straight forward is to use matplotlib.dates to format the axis:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def graph(dataframe):
fig, ax = plt.subplots()
xfmt = mdates.DateFormatter('%YM%m') #see https://strftime.org/
major = mdates.MonthLocator([1,7]) #label only Jan and Jul
graph = dataframe[["Profit"]].plot(ax=ax) #link plot to the existing axes
graph.set_title('Statistics')
graph.set_ylabel('Thousand $')
graph.set_xlabel('Time')
graph.xaxis.set_major_locator(major) #set major locator tick on x-axis
graph.xaxis.set_major_formatter(xfmt) #format xtick label
plt.grid(True)
plt.show()
But a key point is you need to have your dates as Python's built-in datetime.date (not datetime.datetime); thanks to this answer. If your dates are str or a different type of datetime, you will need to convert, but there are many resources on SO and elsewhere for doing this like this or this:
In[0]:
dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
dr = [pd.to_datetime(date).date() for date in df.index] #explicitly converting to datetime with .date()
df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})
type(df.index.[0])
Out[0]:
datetime.date
Calling graph(df) using the example above gets this plot:
Just to expand on this, here's what happens when the index is pandas.Timestamp instead of datetime.date:
In[0]:
dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
# dr = [pd.to_datetime(date).date() for date in df.index] #skipping date conversion
df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})
graph(df)
Out[0]:
The x-axis is improperly formatted:
However, if you are willing to just create the plot directly through matplotlib, rather than pandas (pandas is using matplotlib anyway), this can handle more types of dates:
In[0]:
dr = pd.date_range('01-01-2012', '01-01-2014', freq='1MS')
# dr = [pd.to_datetime(date).date() for date in df.index] #skipping date conversion
df = pd.DataFrame(index=dr, data={'Profit':np.random.rand(25)})
def graph_2(dataframe):
fig, ax = plt.subplots()
xfmt = mdates.DateFormatter('%YM%m')
major = mdates.MonthLocator([1,7])
ax.plot(dataframe.index,dataframe['Profit'], label='Profit')
ax.set_title('Statistics')
ax.set_ylabel('Thousand $')
ax.set_xlabel('Time')
ax.xaxis.set_major_locator(major)
ax.xaxis.set_major_formatter(xfmt)
ax.legend() #legend needs to be added
plt.grid(True)
plt.show()
graph_2(df)
type(df.index[0])
Out[0]:
pandas._libs.tslibs.timestamps.Timestamp
And here is the working graph:

Plotting a times series using matplotlib with 24 hours on the y-axis

If I run the following, it appears to work as expected, but the y-axis is limited to the earliest and latest times in the data. I want it to show midnight to midnight. I thought I could do that with the code that's commented out. But when I uncomment it, I get the correct y-axis, yet nothing plots. Where am I going wrong?
from datetime import datetime
import matplotlib.pyplot as plt
data = ['2018-01-01 09:28:52', '2018-01-03 13:02:44', '2018-01-03 15:30:27', '2018-01-04 11:55:09']
x = []
y = []
for i in range(0, len(data)):
t = datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
x.append(t.strftime('%Y-%m-%d')) # X-axis = date
y.append(t.strftime('%H:%M:%S')) # Y-axis = time
plt.plot(x, y, '.')
# begin = datetime.strptime('00:00:00', '%H:%M:%S').strftime('%H:%M:%S')
# end = datetime.strptime('23:59:59', '%H:%M:%S').strftime('%H:%M:%S')
# plt.ylim(begin, end)
plt.show()
Edit: I also noticed that the x-axis isn't right either. The data skips Jan 2, but I want that on the axis so the data is to scale.
This is a dramatically simplified version of code dealing with over a year's worth of data with over 2,500 entries.

If Pandas is available to you, consider this approach:
import pandas as pd
data = pd.to_datetime(data, yearfirst=True)
plt.plot(data.date, data.time)
_=plt.ylim(["00:00:00", "23:59:59"])
Update per comments
X-axis date formatting can be adjusted using the Locator and Formatter methods of the matplotlib.dates module. Locator finds the tick positions, and Formatter specifies how you want the labels to appear.
Sometimes Matplotlib/Pandas just gets it right, other times you need to call out exactly what you want using these extra methods. In this case, I'm not sure why those numbers are showing up, but this code will remove them.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
f, ax = plt.subplots()
data = pd.to_datetime(data, yearfirst=True)
ax.plot(data.date, data.time)
ax.set_ylim(["00:00:00", "23:59:59"])
days = mdates.DayLocator()
d_fmt = mdates.DateFormatter('%m-%d')
ax.xaxis.set_major_locator(days)
ax.xaxis.set_major_formatter(d_fmt)

pandas .plot() x-axis tick frequency -- how can I show more ticks?

I am plotting time series using pandas .plot() and want to see every month shown as an x-tick.
Here is the dataset structure
Here is the result of the .plot()
I was trying to use examples from other posts and matplotlib documentation and do something like
ax.xaxis.set_major_locator(
dates.MonthLocator(revenue_pivot.index, bymonthday=1,interval=1))
But that removed all the ticks :(
I also tried to pass xticks = df.index, but it has not changed anything.
What would be the rigth way to show more ticks on x-axis?

No need to pass any args to MonthLocator. Make sure to use x_compat in the df.plot() call per #Rotkiv's answer.
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import matplotlib.dates as mdates
df = pd.DataFrame(np.random.rand(100,2), index=pd.date_range('1-1-2018', periods=100))
ax = df.plot(x_compat=True)
ax.xaxis.set_major_locator(mdates.MonthLocator())
plt.show()
formatted x-axis with set_major_locator
unformatted x-axis

You could also format the x-axis ticks and labels of a pandas DateTimeIndex "manually" using the attributes of a pandas Timestamp object.
I found that much easier than using locators from matplotlib.dates which work on other datetime formats than pandas (if I am not mistaken) and thus sometimes show an odd behaviour if dates are not converted accordingly.
Here's a generic example that shows the first day of each month as a label based on attributes of pandas Timestamp objects:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# data
dim = 8760
idx = pd.date_range('1/1/2000 00:00:00', freq='h', periods=dim)
df = pd.DataFrame(np.random.randn(dim, 2), index=idx)
# select tick positions based on timestamp attribute logic. see:
# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Timestamp.html
positions = [p for p in df.index
if p.hour == 0
and p.is_month_start
and p.month in range(1, 13, 1)]
# for date formatting, see:
# https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior
labels = [l.strftime('%m-%d') for l in positions]
# plot with adjusted labels
ax = df.plot(kind='line', grid=True)
ax.set_xlabel('Time (h)')
ax.set_ylabel('Foo (Bar)')
ax.set_xticks(positions)
ax.set_xticklabels(labels)
plt.show()
yields:
Hope this helps!

The right way to do that described here
Using the x_compat parameter, it is possible to suppress automatic tick resolution adjustment
df.A.plot(x_compat=True)

If you want to just show more ticks, you can also dive deep into the structure of pd.plotting._converter:
dai = ax.xaxis.minor.formatter.plot_obj.date_axis_info
dai['fmt'][dai['fmt'] == b''] = b'%b'
After plotting, the formatter is a TimeSeries_DateFormatter and _set_default_format has been called, so self.plot_obj.date_axis_info is not None. You can now manipulate the structured array .date_axis_info to be to your liking, namely contain less b'' and more b'%b'

Remove tick labels:
ax = df.plot(x='date', y=['count'])
every_nth = 10
for n, label in enumerate(ax.xaxis.get_ticklabels()):
if n % every_nth != 0:
label.set_visible(False)
Lower every_nth to include more labels, raise to keep fewer.