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how do we plot a scatter plot for time in python

i want to plot a scatter plot between mentioned two columns k and s . k should be on x axis showing time on hourly basis for 24 hours and s should be on y axis. i have already tried some code using using sns.relplot but got attribute error.
data columns in which we want scatter plot
code which we tried with error

Try:
import matplotlib.dates as mdates
from datetime import time
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame([['2020-05-26 06:15:07','105'], ['2020-05-26 06:15:07','41'], ['2020-05-26 06:16:51','95']], columns=["k", "s"])
df.k = pd.to_datetime(df.k, format='%Y-%m-%d %H:%M:%S')
ax = sns.scatterplot(df.k, df.s)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
ax.tick_params(axis="x", rotation=45)
ax.plot()
import matplotlib.dates as mdates
from datetime import time
import matplotlib.pyplot as plt
df = pd.DataFrame([['2020-05-26 06:15:07','105'], ['2020-05-26 06:15:07','41'], ['2020-05-26 06:16:51','95']], columns=["k", "s"])
df.k = pd.to_datetime(df.k, format='%Y-%m-%d %H:%M:%S')
df.set_index(['k'],inplace=True)
ax = sns.scatterplot(df.index, df.s)
# ax.set(xlabel="time", ylabel="values")
ax.set_xlim(df.index[0], df.index[-1])
ax.xaxis.set_major_locator(mdates.HourLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
ax.tick_params(axis="x", rotation=45)
ax.plot()

How to change xticks to yearly interval in pandas time series plot

I am very new to pandas, and I have searched many StackOverflow questions similar to this for changing xtick labels yearly, but they all are different did not solve my problem, so I decided to ask my own question.
Here is my question. I have a mock data frame which I want to plot yearly xticks in the x-axis.
import numpy as np
import pandas as pd
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
df.plot()
This gives:
Xticks ==> 1995 2000 2005 etc
But I want ==> 1991 1992 ... 2019
How to do that?
So far I have tried this:
import matplotlib
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
fig,ax = plt.subplots()
df.plot(ax=ax)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
# ax.xaxis.set_minor_locator(matplotlib.dates.YearLocator(base=1))
# ax.set_xticklabels(list(df.index.time))
This gives just 2005 as xtick and nothing has worked till now.
Links I looked:
- Changing xticks in a pandas plot
- Python: Change the time on xticks for Pandas Plot
- https://matplotlib.org/3.1.1/api/dates_api.html

You need to use the x_compat=True argument to have pandas choose the units in a way that they are compatible with matplotlib.dates locators and formatters.
df.plot(ax=ax, x_compat=True)
Complete code:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
fig,ax = plt.subplots()
df.plot(ax=ax, x_compat=True)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator(base=1))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
plt.show()

You can try this:
import datetime
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
Complete Example
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import datetime
# data
df = pd.DataFrame({'date': pd.date_range('1991-01-01','2019-01-01')}).set_index('date')
df['value'] = np.random.randn(len(df))
# create xticks
xticks = pd.date_range(datetime.datetime(1990,1,1), datetime.datetime(2020,1,1), freq='YS')
# plot
fig, ax = plt.subplots(figsize=(12,8))
df['value'].plot(ax=ax,xticks=xticks.to_pydatetime())
ax.set_xticklabels([x.strftime('%Y') for x in xticks]);
plt.xticks(rotation=90);
plt.show()
This gives:

How to format y-axis to show percentage with Python Jupyter Notebook lmplot? [duplicate]

I have the following pandas plot:
Is it possible to add '%' sign on the y axis not as a label but on the number. Such as it would show instead of 0.0 it would be 0.0% and so on for all the numbers?
Code:
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
end = datetime.date.today()
start = datetime.date(2020,1,1)
data = web.DataReader('fb', 'yahoo', start, end)
data['percent'] = data['Close'].pct_change()
data['percent'].plot()

Here is how you can use matplotlib.ticker:
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
plt.show()
Output:

You can now control the display format of the y-axis. I think it will be 0.0%.
yvals = ax.get_yticks()
ax.set_yticklabels(["{:,.1%}".format(y) for y in yvals], fontsize=12)

You can also use plt.gca() instead of using ax
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0))

How to increase xticks with a datetime index in matplotlib?

I am trying to use matplotlib to graph stock prices against a datetime index.
So I have a graph that looks like this:
And I need it to look like this:
I think it might have something to do with xticks, but I cannot figure out how to make xticks work with a datetime index. Thanks for the help.
tesla['Open'].plot(title='Open Price', label = 'Tesla', figsize = (16, 6))
ford['Open'].plot(label = 'ford')
gm['Open'].plot(label = 'GM')
plt.legend()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))

Like this:
import pandas as pd
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y-%m')
ax1.xaxis.set_major_formatter(monthyearFmt)
_ = plt.xticks(rotation=45)
Output:
EDIT:
Let's use set_major_locator with MonthLocator
import pandas as pd
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y-%m')
ax1.xaxis.set_major_formatter(monthyearFmt)
ax1.xaxis.set_major_locator(mdates.MonthLocator([1,7]))
_ = plt.xticks(rotation=45)
Output:

Matplotlib DateFormatter for axis label not working

I'm trying to adjust the formatting of the date tick labels of the x-axis so that it only shows the Year and Month values. From what I've found online, I have to use mdates.DateFormatter, but it's not taking effect at all with my current code as is. Anyone see where the issue is? (the dates are the index of the pandas Dataframe)
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
basicDF['some_column'].plot(ax=ax, kind='bar', rot=75)
ax.xaxis_date()
Reproducible scenario code:
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=rng)
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
blah.plot(ax=ax, kind='bar')
ax.xaxis_date()
Still can't get just the year and month to show up.
If I set the format after .plot , get an error like this:
ValueError: DateFormatter found a value of x=0, which is an illegal date. This usually occurs because you have not informed the axis that it is plotting dates, e.g., with ax.xaxis_date().
It's the same for if I put it before ax.xaxis_date() or after.

pandas just doesn't work well with custom date-time formats.
You need to just use raw matplotlib in cases like this.
import numpy
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas
N = 20
numpy.random.seed(N)
dates = pandas.date_range('1/1/2014', periods=N, freq='m')
df = pandas.DataFrame(
data=numpy.random.randn(N),
index=dates,
columns=['A']
)
fig, ax = plt.subplots(figsize=(10, 6))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.bar(df.index, df['A'], width=25, align='center')
And that gives me:

Solution with pandas only
You can create nicely formatted ticks by using the DatetimeIndex and taking advantage of the datetime properties of the timestamps. Tick locators and formatters from matplotlib.dates are not necessary for a case like this unless you would want dynamic ticks when using the interactive interface of matplotlib for zooming in and out (more relevant for time ranges longer than in this example).
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
# Create sample time series with month start frequency, plot it with a pandas bar chart
rng = np.random.default_rng(seed=1) # random number generator
dti = pd.date_range('1/1/2014', periods=20, freq='m')
df = pd.DataFrame(data=rng.normal(size=dti.size), index=dti)
ax = df.plot.bar(figsize=(10,4), legend=None)
# Set major ticks and tick labels
ax.set_xticks(range(df.index.size))
ax.set_xticklabels([ts.strftime('%b\n%Y') if ts.year != df.index[idx-1].year
else ts.strftime('%b') for idx, ts in enumerate(df.index)])
ax.figure.autofmt_xdate(rotation=0, ha='center');

The accepted answer claims that "pandas won't work well with custom date-time formats", but you can make use of pandas' to_datetime() function to use your existing datetime Series in the dataframe:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=pd.to_datetime(rng))
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(DateFormatter('%m-%Y'))
ax.bar(blah.index, blah[0], width=25, align='center')
Will result in:
You can see the different available formats here.

I stepped into the same problem and I used an workaround to transform the index from date time format into the desired string format:
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
rng = pd.date_range('1/1/2014', periods=20, freq='m')
blah = pd.DataFrame(data = np.random.randn(len(rng)), index=rng)
fig = plt.figure(figsize = (10,6))
ax = fig.add_subplot(111)
# transform index to strings
blah_test = blah.copy()
str_index = []
for s_year,s_month in zip(blah.index.year.values,blah.index.month.values):
# build string accorind to format "%Y-%m"
string_day = '{}-{:02d}'.format(s_year,s_month)
str_index.append(string_day)
blah_test.index = str_index
blah_test.plot(ax=ax, kind='bar', rot=45)
plt.show()
which results in the following figure: