Develop Reference

How to add multiple custom ticks to seaborn boxplot

I generated a boxplot using seaborn. On the x axis, I would like to have, both the number of days (20, 25, 32) and the actual dates they refer to (2022-05-08, 2022-05-13, 2022-05-20).
I found a potential solution at the following link add custom tick with matplotlib. I'm trying to adapt it to my problem but I could only get the number of days or the dates, not both.
I really would appreciate any help. Thank you in advance for your time.
Please, find below my code and the desired output.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({'nb_days':[20,20,20,25,25,20,32,32,25,32,32],
'Dates':['2022-05-08','2022-05-08','2022-05-08','2022-05-13','2022-05-13','2022-05-08','2022-05-20','2022-05-20','2022-05-13','2022-05-20','2022-05-20'],
'score':[3,3.5,3.4,2,2.2,3,5,5.2,4,4.3,5]})
df['Dates'] = df['Dates'].apply(pd.to_datetime)
tick_label = dict(zip(df['nb_days'],df['Dates'].apply(lambda x: x.strftime('%Y-%m-%d')))) #My custom xtick label
#Plot
fig,ax = plt.subplots(figsize=(6,6))
ax = sns.boxplot(x='nb_days',y='score',data=df,color=None)
# iterate over boxes to change color
for i,box in enumerate(ax.artists):
box.set_edgecolor('red')
box.set_facecolor('white')
sns.stripplot(x='nb_days',y='score',data=df,color='black')
ticks = sorted(df['nb_days'].unique())
labels = [tick_label.get(t, ticks[i]) for i,t in enumerate(ticks)]
ax.set_xticklabels(labels)
plt.tight_layout()
plt.show()
plt.close()
Here is the desired output.

You can do that by adding these lines in place of ax.set_xticklabels(labels)
new_labels=["{}\n{}".format(a_, b_) for a_, b_ in zip(ticks, labels)]
ax.set_xticklabels(new_labels)
Output

Try this:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({'nb_days':[20,20,20,25,25,20,32,32,25,32,32],
'Dates':['2022-05-08','2022-05-08','2022-05-08','2022-05-13','2022-05-13','2022-05-08','2022-05-20','2022-05-20','2022-05-13','2022-05-20','2022-05-20'],
'score':[3,3.5,3.4,2,2.2,3,5,5.2,4,4.3,5]})
df['Dates'] = df['Dates'].apply(pd.to_datetime)
tick_label = dict(zip(df['nb_days'],df['Dates'].apply(lambda x: x.strftime('%Y-%m-%d')))) #My custom xtick label
#Plot
fig,ax = plt.subplots(figsize=(6,6))
ax = sns.boxplot(x='nb_days',y='score',data=df,color=None)
# iterate over boxes to change color
for i,box in enumerate(ax.artists):
box.set_edgecolor('red')
box.set_facecolor('white')
sns.stripplot(x='nb_days',y='score',data=df,color='black')
ticks = sorted(df['nb_days'].unique())
labels = ["{}\n".format(t)+tick_label.get(t, ticks[i]) for i, t in enumerate(ticks)]
ax.set_xticklabels(labels)
plt.tight_layout()
plt.show()
plt.close()

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:])

Fix x-axis scale seaborn factorplot

I'm attempting to make a figure that shows two plots, with each plot separated based on a set of categorical data. However, although I can make the graph, I cant figure out how to get the x-axis to be properly spaced.
I want the x-axis to start before the first value (want axis to start at 60 [first value = 63]) and end after the last (want axis to end at 95 [last value = 92.1]), with xticks going up in 5's.
Any help is much appreciated! Thanks in advance!
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.axes
import seaborn as sns
Temperature = [63.0,63.3,63.6,63.9,64.2,64.5,64.8,65.2,65.5,65.8,66.1,66.4,66.7,67.0,67.3,67.7,68.0,68.3,68.6,68.9,69.2,69.5,69.9,70.2,70.5,70.8,71.1,71.4,71.8,72.1,72.4,72.7,73.0,73.4,73.7,74.0,74.3,74.6,74.9,75.2,75.6,75.9,76.2,76.5,76.9,77.2,77.5,77.8,78.1,78.5,78.8,79.1,79.4,79.7,80.1,80.4,80.7,81.0,81.3,81.6,81.9,82.3,82.6,82.9,83.2,83.5,83.8,84.1,84.4,84.8,85.1,85.4,85.7,86.0,86.3,86.6,86.9,87.2,87.5,87.8,88.1,88.4,88.7,89.0,89.3,89.6,89.8,90.1,90.4,90.7,91.0,91.2,91.5,91.8,92.1,63.0,63.3,63.6,63.9,64.2,64.5,64.8,65.2,65.5,65.8,66.1,66.4,66.7,67.0,67.3,67.7,68.0,68.3,68.6,68.9,69.2,69.5,69.9,70.2,70.5,70.8,71.1,71.4,71.8,72.1,72.4,72.7,73.0,73.4,73.7,74.0,74.3,74.6,74.9,75.2,75.6,75.9,76.2,76.5,76.9,77.2,77.5,77.8,78.1,78.5,78.8,79.1,79.4,79.7,80.1,80.4,80.7,81.0,81.3,81.6,81.9,82.3,82.6,82.9,83.2,83.5,83.8,84.1,84.4,84.8,85.1,85.4,85.7,86.0,86.3,86.6,86.9,87.2,87.5,87.8,88.1,88.4,88.7,89.0,89.3,89.6,89.8,90.1,90.4,90.7,91.0,91.2,91.5,91.8,92.1]
Derivative = [0.0495,0.0507,0.0525,0.0548,0.0570,0.0579,0.0579,0.0574,0.0574,0.0576,0.0581,0.0587,0.0593,0.0592,0.0584,0.0580,0.0579,0.0580,0.0582,0.0588,0.0592,0.0594,0.0588,0.0581,0.0578,0.0579,0.0580,0.0579,0.0582,0.0581,0.0579,0.0574,0.0571,0.0563,0.0548,0.0538,0.0536,0.0540,0.0544,0.0551,0.0556,0.0551,0.0542,0.0535,0.0536,0.0542,0.0564,0.0623,0.0748,0.0982,0.1360,0.1897,0.2550,0.3228,0.3807,0.4177,0.4248,0.3966,0.3365,0.2558,0.1713,0.0971,0.0438,0.0140,0.0034,0.0028,0.0048,0.0058,0.0057,0.0050,0.0042,0.0038,0.0039,0.0041,0.0038,0.0031,0.0023,0.0017,0.0014,0.0012,0.0015,0.0019,0.0020,0.0018,0.0017,0.0015,0.0014,0.0014,0.0015,0.0014,0.0013,0.0011,0.0007,0.0004,0.0011,0.0105,0.0100,0.0096,0.0090,0.0084,0.0081,0.0077,0.0071,0.0066,0.0063,0.0064,0.0060,0.0057,0.0055,0.0054,0.0051,0.0047,0.0046,0.0042,0.0037,0.0035,0.0040,0.0043,0.0039,0.0032,0.0028,0.0028,0.0027,0.0029,0.0034,0.0038,0.0034,0.0027,0.0024,0.0021,0.0017,0.0015,0.0016,0.0015,0.0011,0.0008,0.0012,0.0019,0.0025,0.0027,0.0026,0.0019,0.0012,0.0010,0.0014,0.0016,0.0014,0.0010,0.0007,0.0007,0.0010,0.0017,0.0021,0.0020,0.0013,0.0012,0.0013,0.0014,0.0015,0.0018,0.0017,0.0012,0.0013,0.0018,0.0028,0.0031,0.0033,0.0027,0.0022,0.0015,0.0016,0.0022,0.0026,0.0026,0.0019,0.0012,0.0006,0.0007,0.0011,0.0016,0.0014,0.0010,0.0009,0.0012,0.0015,0.0014,0.0008,0.0001,-0.0003,0.0002]
Category = ["a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b"]
df = pd.DataFrame({"Temperature": Temperature,
"Derivative": Derivative,
"Category" : Category})
g = sns.factorplot(x="Temperature", y="Derivative", data=df, col="Category")
g.set_xticklabels(step=10)

All the desired feature you describe suggest that using a factorplot here is absolutely the wrong choice. Instead use a normal matplotlib plot and then set the limits as usual, plt.xlim(60,95).
import pandas as pd
import matplotlib.pyplot as plt
Temperature = [63.0,63.3,63.6,63.9,64.2,64.5,64.8,65.2,65.5,65.8,66.1,66.4,66.7,67.0,67.3,67.7,68.0,68.3,68.6,68.9,69.2,69.5,69.9,70.2,70.5,70.8,71.1,71.4,71.8,72.1,72.4,72.7,73.0,73.4,73.7,74.0,74.3,74.6,74.9,75.2,75.6,75.9,76.2,76.5,76.9,77.2,77.5,77.8,78.1,78.5,78.8,79.1,79.4,79.7,80.1,80.4,80.7,81.0,81.3,81.6,81.9,82.3,82.6,82.9,83.2,83.5,83.8,84.1,84.4,84.8,85.1,85.4,85.7,86.0,86.3,86.6,86.9,87.2,87.5,87.8,88.1,88.4,88.7,89.0,89.3,89.6,89.8,90.1,90.4,90.7,91.0,91.2,91.5,91.8,92.1,63.0,63.3,63.6,63.9,64.2,64.5,64.8,65.2,65.5,65.8,66.1,66.4,66.7,67.0,67.3,67.7,68.0,68.3,68.6,68.9,69.2,69.5,69.9,70.2,70.5,70.8,71.1,71.4,71.8,72.1,72.4,72.7,73.0,73.4,73.7,74.0,74.3,74.6,74.9,75.2,75.6,75.9,76.2,76.5,76.9,77.2,77.5,77.8,78.1,78.5,78.8,79.1,79.4,79.7,80.1,80.4,80.7,81.0,81.3,81.6,81.9,82.3,82.6,82.9,83.2,83.5,83.8,84.1,84.4,84.8,85.1,85.4,85.7,86.0,86.3,86.6,86.9,87.2,87.5,87.8,88.1,88.4,88.7,89.0,89.3,89.6,89.8,90.1,90.4,90.7,91.0,91.2,91.5,91.8,92.1]
Derivative = [0.0495,0.0507,0.0525,0.0548,0.0570,0.0579,0.0579,0.0574,0.0574,0.0576,0.0581,0.0587,0.0593,0.0592,0.0584,0.0580,0.0579,0.0580,0.0582,0.0588,0.0592,0.0594,0.0588,0.0581,0.0578,0.0579,0.0580,0.0579,0.0582,0.0581,0.0579,0.0574,0.0571,0.0563,0.0548,0.0538,0.0536,0.0540,0.0544,0.0551,0.0556,0.0551,0.0542,0.0535,0.0536,0.0542,0.0564,0.0623,0.0748,0.0982,0.1360,0.1897,0.2550,0.3228,0.3807,0.4177,0.4248,0.3966,0.3365,0.2558,0.1713,0.0971,0.0438,0.0140,0.0034,0.0028,0.0048,0.0058,0.0057,0.0050,0.0042,0.0038,0.0039,0.0041,0.0038,0.0031,0.0023,0.0017,0.0014,0.0012,0.0015,0.0019,0.0020,0.0018,0.0017,0.0015,0.0014,0.0014,0.0015,0.0014,0.0013,0.0011,0.0007,0.0004,0.0011,0.0105,0.0100,0.0096,0.0090,0.0084,0.0081,0.0077,0.0071,0.0066,0.0063,0.0064,0.0060,0.0057,0.0055,0.0054,0.0051,0.0047,0.0046,0.0042,0.0037,0.0035,0.0040,0.0043,0.0039,0.0032,0.0028,0.0028,0.0027,0.0029,0.0034,0.0038,0.0034,0.0027,0.0024,0.0021,0.0017,0.0015,0.0016,0.0015,0.0011,0.0008,0.0012,0.0019,0.0025,0.0027,0.0026,0.0019,0.0012,0.0010,0.0014,0.0016,0.0014,0.0010,0.0007,0.0007,0.0010,0.0017,0.0021,0.0020,0.0013,0.0012,0.0013,0.0014,0.0015,0.0018,0.0017,0.0012,0.0013,0.0018,0.0028,0.0031,0.0033,0.0027,0.0022,0.0015,0.0016,0.0022,0.0026,0.0026,0.0019,0.0012,0.0006,0.0007,0.0011,0.0016,0.0014,0.0010,0.0009,0.0012,0.0015,0.0014,0.0008,0.0001,-0.0003,0.0002]
Category = ["a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b"]
df = pd.DataFrame({"Temperature": Temperature,
"Derivative": Derivative,
"Category" : Category})
for n, data in df.groupby("Category"):
plt.plot(data["Temperature"],data["Derivative"] , marker="o", label=n)
plt.xlim(60,95)
plt.legend()
plt.show()
Or if subplots are desired,
fig,axes = plt.subplots(ncols=len(df["Category"].unique()), sharey=True)
for ax,(n, data) in zip(axes,df.groupby("Category")):
ax.plot(data["Temperature"],data["Derivative"] , marker="o", label=n)
ax.set_title("Category {}".format(n))
ax.set_xlim(60,95)
plt.show()
Finally, you may use a seaborn FacetGrid onto which you plot your data with a plot:
g = sns.FacetGrid(df, col="Category")
g.map(plt.plot, "Temperature", "Derivative",marker="o",)
for ax in g.axes.flat:
ax.set_xlim(60,95)
plt.show()

Unable to show Pandas dateindex on a matplotlib x axis

I'm trying to build matplotlib charts whose x-axis is a dateIndex from a pandas dataframe. Trying to mimic some examples from matplotlib, I've been unsuccessful. The xaxis ticks and labels never appear.
I thought maybe matplotlib wasn't properly digesting the pandas index, so I converted it to ordinal with the matplotlib date2num helper function, but that gave the same result.
# https://matplotlib.org/api/dates_api.html
# https://matplotlib.org/examples/api/date_demo.html
import datetime as dt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
import matplotlib.dates as mpd
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
majorLocator = years
majorFormatter = yearsFmt #FormatStrFormatter('%d')
minorLocator = months
y1 = np.arange(100)*0.14+1
y2 = -(np.arange(100)*0.04)+12
"""neither of these indices works"""
x = pd.date_range(start='4/1/2012', periods=len(y1))
#x = map(mpd.date2num, pd.date_range(start='4/1/2012', periods=len(y1)))
fig, ax = plt.subplots()
ax.plot(x,y1)
ax.plot(x,y2)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = x[0]
datemax = x[-1]
ax.set_xlim(datemin, datemax)
fig.autofmt_xdate()
plt.show()

The problem is the following. pd.date_range(start='4/1/2012', periods=len(y1)) creates dates from the first of April 2012 to the 9th of July 2012.
Now you set the major locator to be a YearLocator. This means, that you want to have a tick for each year on the axis. However, all dates are within the same year 2012. So there is no major tick to be shown within the plot range.
The suggestion would be to use a MonthLocator instead, such that the first of each month is ticked. Also if would make sense to use a formatter, which actually shows the months, e.g. '%b %Y'. You may use a DayLocator for the minor ticks, if you want, to show the small tickmarks for each day.
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_minor_locator(mdates.DayLocator())
Complete example:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
y1 = np.arange(100)*0.14+1
y2 = -(np.arange(100)*0.04)+12
x = pd.date_range(start='4/1/2012', periods=len(y1))
fig, ax = plt.subplots()
ax.plot(x,y1)
ax.plot(x,y2)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_minor_locator(mdates.DayLocator())
fig.autofmt_xdate()
plt.show()

You could use pd.DataFrame.plot to handle most of that
df = pd.DataFrame(dict(
y1=y1, y2=y2
), index=x)
df.plot()

Matplotlib pyplot - tick control and showing date

My matplotlib pyplot has too many xticks - it is currently showing each year and month for a 15-year period, e.g. "2001-01", but I only want the x-axis to show the year (e.g. 2001).
The output will be a line graph where x-axis shows dates and the y-axis shows the sale and rent prices.
# Defining the variables
ts1 = prices['Month'] # eg. "2001-01" and so on
ts2 = prices['Sale']
ts3 = prices['Rent']
# Reading '2001-01' as year and month
ts1 = [dt.datetime.strptime(d,'%Y-%m').date() for d in ts1]
plt.figure(figsize=(13, 9))
# Below is where it goes wrong. I don't know how to set xticks to show each year.
plt.xticks(ts1, rotation='vertical')
plt.xlabel('Year')
plt.ylabel('Price')
plt.plot(ts1, ts2, 'r-', ts1, ts3, 'b.-')
plt.gcf().autofmt_xdate()
plt.show()

Try removing the plt.xticks function call altogether. matplotlib will then use the default AutoDateLocator function to find the optimum tick locations.
Alternatively if the default includes some months which you don't want then you can use matplotlib.dates.YearLocator which will force the ticks to be years only.
You can set the locator as shown below in a quick example:
import matplotlib.pyplot as plt
import matplotlib.dates as mdate
import numpy as np
import datetime as dt
x = [dt.datetime.utcnow() + dt.timedelta(days=i) for i in range(1000)]
y = range(len(x))
plt.plot(x, y)
locator = mdate.YearLocator()
plt.gca().xaxis.set_major_locator(locator)
plt.gcf().autofmt_xdate()
plt.show()

You can do this with plt.xticks.
As an example, here I have set the xticks frequency to display every three indices. In your case, you would probably want to do so every twelve indices.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = np.random.randn(10)
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 3))
plt.show()
In your case, since you are using dates, you can replace the argument of the second to last line above with something like ts1[0::12], which will select every 12th element from ts1 or np.arange(0, len(dates), 12) which will select every 12th index corresponding to the ticks you want to show.