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How to reduce x ticks in matplotlib?

I have been working on pandas data that has too many time x points/ticks. I have found several solutions to reduce it, but my problem is I'm using two different data set with different time points. So, how to not only reduce the x-ticks as well as aligning these two data time points?
Yes, I know sharex.
My plot generating code is following:
fig, (ax1, ax2,ax3,ax4) = plt.subplots(4, 1,figsize=(10,7), sharex="all")
fig.subplots_adjust(bottom=0.2)
ax1.plot( df.time, df['B'], color='k')
ax1.plot( df.time, df['Bx'], color='r')
ax1.plot( df.time, df['By'], color='b')
ax1.plot( df.time, df['Bz'], color='g')
ax1.xaxis.grid(True,alpha=0.3)
ax1.set_ylabel('Bx,By,Bz,B[nT]')
ax2.plot(df1.time, df1['v_total'],color='k')
ax2.plot(df1.time, df1['Vx'],color='r')
ax2.plot(df1.time, df1['Vy'],color='b')
ax2.plot(df1.time, df1['Vz'],color='g')
ax2.xaxis.grid(True,alpha=0.3)
ax2.set_ylabel('Vx,Vy,Vz,V[km/s]')
ax3.plot(df1.time, df1['n'],color='k')
ax3.xaxis.grid(True,alpha=0.3)
ax3.set_ylabel('Np[1/cm^3]')
ax4.plot(df1.time, df1['T'],color='k')
ax4.xaxis.grid(True,alpha=0.3)
ax4.set_ylabel('T[k]')
#loc = mdates.MinuteLocator([0,30])
#ax2.xaxis.set_major_locator(loc)
#ax2.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
#ax3.xaxis.set_major_locator(loc)
#ax3.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
#ax4.xaxis.set_major_locator(loc)
#ax4.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
#ax2 = plt.gca()
#ax2.xaxis.set_major_locator(mdates.MinuteLocator(interval=10))
#ax2.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
#ax3 = plt.gca()
#ax3.xaxis.set_major_locator(mdates.MinuteLocator(interval=10))
#ax3.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
#ax4 = plt.gca()
#ax4.xaxis.set_major_locator(mdates.MinuteLocator(interval=10))
#ax4.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
fig.suptitle('Shock format')
plt.savefig('plot.png')
plt.savefig('plot1.pdf')
plt.show()
Here df is the one with many points and I want to reduce df x ticks/time points as well as aligning df1 to df. Hashtagged lines are my try, but it takes too long and giving me this warning "Locator attempting to generate 359569 ticks ([-113.5, ..., 2383.5]), which exceeds Locator.MAXTICKS (1000)."
The graph output is following
What my goal should look like is this

I have created the data as I see fit as it seems difficult to provide data.
The main point is that the byminute is the number of minutes, and the default value is 1 minute in the range (60). The interval specifies at what interval that minute increment should be displayed. So for the 15 minute increments, I used np.range(0,60,15) and two intervals. The result is 00 and 30 minute increments.
import pandas as pd
import numpy as np
date_rng = pd.date_range('2022-05-31 00:00:00', freq='1s', periods=43200)
df = pd.DataFrame({'datetime': pd.to_datetime(date_rng), 'value':np.random.randn(43200).cumsum()})
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
fig, (ax1,ax2,ax3,ax4) = plt.subplots(4, 1,figsize=(12,6), sharex="all")
fig.subplots_adjust(bottom=0.2)
ax1.plot(df.datetime, df['value'], color='b')
ax2.plot(df.datetime, df['value'], color='g')
ax3.plot(df.datetime, df['value'], color='r')
ax4.plot(df.datetime, df['value'], color='k')
minutes = mdates.MinuteLocator(byminute=np.arange(0,60,15),interval=2)
minutes_fmt = mdates.DateFormatter('%d %H:%M')
ax4.xaxis.set_major_locator(minutes)
ax4.xaxis.set_major_formatter(minutes_fmt)
ax4.tick_params(axis='x', labelrotation=45)
plt.show()

I am trying to plot the individual data points along with box plots from two different data frames based on a condition using seaborn

I am using seaborn in Jupyterlab to plot my data. Here is the code snippet for plotting the graph where I have separated data based on the presence/absence of PMMA shown by PMMA=1, PMMA=0 respectively. However, the strip plot on PMMA=1 for 17 and 20 on the x-axis is plotting the individual data points from PMMA=0 and the strip plot for PMMA=0 is not showing for the rest of the data. How can I fix this issue? Also, the legend is not showing the tag as "Day#"
both = pd.concat((df1, df2))
grped_bplot = sns.catplot(x='Passage#',
y='Dendrite Length (um)',
hue="Day#",
col="PMMA",
kind="box",
legend=False,
height=6,
aspect=1.3,
palette="Set2",
data=both);
grped_bplot = sns.stripplot(x='Passage#',
y='Dendrite Length (um)',
hue='Day#',
jitter=True,
dodge=True,
marker='o',
palette="Set2",
alpha=0.5,
data=both)
handles, labels = grped_bplot.get_legend_handles_labels()
l = plt.legend(handles[0:3], labels[0:3])
Boxplot with overlapping strip plot

sns.catplot returns a FacetGrid. You can call .map_dataframe(sns.stripplot) to create strip plots for the same data.
Here is some example code starting from generated test data:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# first, create some test data
both = pd.DataFrame({'Passage#': np.random.choice([6, 17, 18, 19, 20], 500),
'Dendrite Length (um)': np.random.uniform(1, 17, 500) ** 3,
'Day#': np.random.choice([3, 4, 7], 500),
'PMMA': np.random.randint(0, 2, 500)})
g = sns.catplot(x='Passage#',
y='Dendrite Length (um)',
hue="Day#",
col="PMMA",
kind="box",
legend=False,
height=6,
aspect=1.3,
palette="Set2",
boxprops={'alpha': 0.4},
data=both)
g.map_dataframe(sns.stripplot,
x='Passage#',
y='Dendrite Length (um)',
hue='Day#',
jitter=True,
dodge=True,
marker='o',
palette="Set2",
alpha=0.5)
g.add_legend(title='Day#')
plt.show()
PS: To have the boxes in the legend instead of the dots, you can call g.add_legend() after the catplot but before calling g.map_dataframe.

date and graph alignment - Economic analysis

I'm am running a fundamental economic analysis and when I get to visualising and charting I am not able to align the dates with the graph.
I wanted the most recent date entry to show on the right and the rest of the dates to show every two years.
I have tried literally everything and cant find the solution.
Here is my code:
%matplotlib inline
import pandas as pd
from matplotlib import pyplot
import matplotlib.dates as mdates
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csvbgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=off&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=NAEXKP01EZQ657S&scale=left&cosd=1995-04-01&coed=2020-04-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Quarterly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2020-09-21&revision_date=2020-09-21&nd=1995-04-01')
df = df.set_index('DATE')
df['12MonthAvg'] = df.rolling(window=12).mean().dropna(how='all')
df['9MonthAvg'] = df['12MonthAvg'].rolling(window=12).mean().dropna(how='all')
df['Spread'] = df['12MonthAvg'] - df['9MonthAvg']
pyplot.style.use("seaborn")
pyplot.subplots(figsize=(10, 5), dpi=85)
df['Spread'].plot().set_title('EUROPE: GDP Q Growth Rate (12M/12M Avg Spread)', fontsize=16)
df['Spread'].plot().axhline(0, linestyle='-', color='r',alpha=1, linewidth=2, marker='')
df['Spread'].plot().spines['left'].set_position(('outward', 10))
df['Spread'].plot().spines['bottom'].set_position(('outward', 10))
df['Spread'].plot().spines['right'].set_visible(False)
df['Spread'].plot().spines['top'].set_visible(False)
df['Spread'].plot().yaxis.set_ticks_position('left')
df['Spread'].plot().xaxis.set_ticks_position('bottom')
df['Spread'].plot().text(0.50, 0.02, "Crossing red line downwards / Crossing red line Upwards",
transform=pyplot.gca().transAxes, fontsize=14, ha='center', color='blue')
df['Spread'].plot().fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
print(df['Spread'].tail(3))
pyplot.autoscale()
pyplot.show()
And the output:
This is the raw data:

There is a couple of corrections to your code.
In your URL insert "?" after fredgraph.csv. It starts so called query string,
where bgcolor is the first parameter.
Read your DataFrame with additional parameters:
df = pd.read_csv('...', parse_dates=[0], index_col=[0])
The aim is to:
read Date column as datetime,
set it as the index.
Create additional columns as:
df['12MonthAvg'] = df.NAEXKP01EZQ657S.rolling(window=12).mean()
df['9MonthAvg'] = df.NAEXKP01EZQ657S.rolling(window=9).mean()
df['Spread'] = df['12MonthAvg'] - df['9MonthAvg']
Corrections:
9MonthAvg (as I think) should be computed from the source column,
not from 12MonthAvg,
dropna here is not needed, as you create whole column anyway.
Now is the place to use dropna() on Spread column and save it in
a dedicated variable:
spread = df['Spread'].dropna()
Draw your figure the following way:
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
plt.style.use("seaborn")
fig, ax = plt.subplots(figsize=(10, 5), dpi=85)
plt.plot_date(spread.index, spread, fmt='-')
ax.set_title('EUROPE: GDP Q Growth Rate (12M/12M Avg Spread)', fontsize=16)
ax.axhline(0, linestyle='-', color='r',alpha=1, linewidth=2, marker='')
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.text(0.50, 0.02, "Crossing red line downwards / Crossing red line Upwards",
transform=ax.transAxes, fontsize=14, ha='center', color='blue')
ax.xaxis.set_major_formatter(mdates.DateFormatter(fmt='%Y-%m-%d'))
plt.show()
Corrections:
plt.subplots returns fig and ax, so I saved them (actually, only ax
is needed).
When one axis contains dates, it is better to use plot_date.
I changed the way DateFormatter is set.
Using the above code I got the following picture:

Plotting a vertical line for df.plot.bar works, but it doesn't for a lineplot

I followed all step following my question here : Pandas Dataframe : How to add a vertical line with label to a bar plot when your data is time-series?
it was supposed to solve my problem but when I change the The kind of plot to line , the vertical line did not appear . I copy the same code and change plot type to line instead of bar :
as you can see with bar , the vertical line (in red ) appears .
# function to plot a bar
def dessine_line3(madataframe,debut_date , mes_colonnes):
madataframe.index = pd.to_datetime(madataframe.index,format='%m/%d/%y')
df = madataframe.loc[debut_date:,mes_colonnes].copy()
filt = (df[df.index == '4/20/20']).index
df.index.searchsorted(value=filt)
fig,ax = plt.subplots()
df.plot.bar(figsize=(17,8),grid=True,ax=ax)
ax.axvline(df.index.searchsorted(filt), color="red", linestyle="--", lw=2, label="lancement")
plt.tight_layout()
out :
but whan I just change code by changing the type of plot to line : there is no vertical line and also the x axis (date ) changed .
so I wrote another code juste to draw line with vertical line
ax = madagascar_maurice_case_df[["Madagascar Covid-19 Ratio","Maurice Covid-19 Ratio"]].loc['3/17/20':].plot.line(figsize=(17,7),grid=True)
filt = (df[df.index=='4/20/20']).index
ax.axvline(df.index.searchsorted(filt),color="red",linestyle="--",lw=2 ,label="lancement")
plt.show()
but the result is the same
following the comment below , here is my final code :
def dessine_line5(madataframe,debut_date , mes_colonnes):
plt.figure(figsize=(17,8))
plt.grid(b=True,which='major',axis='y')
df = madataframe.loc[debut_date:,mes_colonnes]
sns.lineplot(data=df)
lt = datetime.toordinal(pd.to_datetime('4/20/20'))
plt.axvline(lt,color="red",linestyle="--",lw=2,label="lancement")
plt.show()
and the result is :

Plot tick locs
The issue is the plot tick locations are a different style depending on plot kind and api
df.plot vs. plt.plot vs. sns.lineplot
Place ticks, labels = plt.xticks() after df.plot.bar(figsize=(17,8),grid=True,ax=ax) and printing ticks will give array([0, 1, 2,..., len(df.index)]), which is why df.index.searchsorted(filt) works, it produces an integer location.
df.plot() has tick locs like array([13136, 13152, 13174, 13175], dtype=int64), for my sample date range. I don't actually know how those numbers are derived, so I don't know how to convert the date to that format.
sns.lineplot and plt.plot have tick locs that are the ordinal representation of the datetime, array([737553., 737560., 737567., 737577., 737584., 737591., 737598.,
737607.]
For a lineplot with your example:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime
sns.lineplot(data=df)
lt = datetime.toordinal(pd.to_datetime('2020/04/20'))
plt.axvline(lt, color="red", linestyle="--", lw=2, label="lancement")
plt.show()
For my example data:
import numpy as np
data = {'a': [np.random.randint(10) for _ in range(40)],
'b': [np.random.randint(10) for _ in range(40)],
'date': pd.bdate_range(datetime.today(), periods=40).tolist()}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)
sns.lineplot(data=df)
ticks, labels = plt.xticks()
lt = datetime.toordinal(pd.to_datetime('2020-05-19'))
plt.axvline(lt, color="red", linestyle="--", lw=2, label="lancement")
plt.show()

Custom X-Axis Date Range Using Matplotlib

This is my first time asking a Python question online. I have always been able to find answers to my questions on this site..until now. I am trying to plot data that were developed using the Index Sequential Method, which is a technique for projecting historical data into the future. I have 105 charts that each cover 47 years of data. The first chart x-axis ranges from 1906-1952, the second 1907-1953, thir 1908-1954, etc. My problem is when I get to 1963, which is when the 47th year reverts back to the begining (1906). So the 1963 chart xaxis would look like this: 1963, 1964, 1965,...2008,2009,2010,1906. The 1964 chart xaxis would look like this: 1964, 1965, 1967,...2009, 2010, 1906, 1907.
I can get the data to plot fine, I just need help figuring out how to format the xaxis to accept the unique wrap-around situation when it occurs.
There are three charts per page (ax1, ax2, and ax3). yearList and chartList are the x and y data, respectively. The code below is part of a for loop that creates the yearList and chartList data sets, and it creates the charts with the wrong xaxis labels.
import matplotlib, pyPdf
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as tkr
from matplotlib.ticker import MultipleLocator
import matplotlib.figure as figure
plt.rcParams['font.family'] = 'Times New Roman'
locator = mdates.YearLocator(2)
minorLocator = MultipleLocator(1)
dateFmt = mdates.DateFormatter('%Y')
datemin = min(yearList)
datemax = max(yearList)
fig, (ax1, ax2, ax3) = plt.subplots(3,1,sharex=False)
#3X3 Top to bottom
ax1.bar(yearList1, chartList1, width=200, align='center')
ax2.bar(yearList2, chartList2, width=200, align='center')
ax3.bar(yearList3, chartList3, width=200, align='center')
axList = [ax1, ax2, ax3]
for ax in axList:
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(dateFmt)
ax.xaxis.set_minor_locator(minorLocator)
ax.set_xlim(datemin - timedelta(365), datemax + timedelta(365))
ax.grid(1)
ax.set_ylim(0,30)
ax.set_yticks(np.arange(0, 31, 5))
ax.yaxis.set_minor_locator(minorLocator)
#Rotate tick labels 90 degrees
xlabels = ax.get_xticklabels()
for label in xlabels:
label.set_rotation(90)
fig.tight_layout()
plt.subplots_adjust(right=0.925)
plt.savefig('%s\\run.pdf' % outDir)

You are making a bar graph, which means the x-posistion has little to no meaning aside from the labels, so don't try to plot the bars vs their date, plot them against the integers, and then label them as you wish:
from itertools import izip
fig, axeses = plt.subplots(3,1,sharex=False)
#3X3 Top to bottom
for yl, cl, ax in izip([yearList1, yearList2, yearList3],
[chartList1, chartList2, chartist3],
axeses):
ax.bar(range(len(cl)), cl, align='center')
ax.set_ylim(0,30)
ax.set_yticks(np.arange(0, 31, 5))
ax.yaxis.set_minor_locator(minorLocator)
xlabels = [dateFmt(xl) for xl in yl] # make a list of formatted labels
ax.set_xticks(range(len(cl))) # put the tick markers under your bars
ax.set_xticklabels(xlabels) # set the labels to be your formatted years
#Rotate tick labels 90 degrees
for label in ax.get_xticklabels():
label.set_rotation(90)
# you only need to do this once
fig.tight_layout()
fig.subplots_adjust(right=0.925)
fig.savefig('%s\\run.pdf' % outDir)
Also see the demo and the docs set_xticks and set_xticklabels

You can use the ax.set_ticklabels() function to set the labels.
Example:
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot([1, 2, 3, 4], [10, 20, 25, 30])
ax.xaxis.set_ticklabels(["foo" , "bar", "ouch"])
plt.show()
So, just add the transformation that you need, and create the labels list.
maybe something like this:
range = 47
yearList = [1967, 1968,..., last year]
range_of_years = map(lambda x: range(year,year + range), yearList)
for i in range(len(axis_list)):
axis_list[i].xaxis.set_ticklabels(years_list[i])