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How to plot sequential data, changing the color according to cluster

I have a dataframe with information concerning the date and the cluster that it belongs (it was done before based on collected temperatures for each day). I want to plot this data in sequence, like a stacked bar chart, changing the color of each element according to the assigned cluster. Here it is my table (the info goes up to 100 days):
Date
order
ClusterNo2
constant
2020-08-07
1
3.0
1
2020-08-08
2
0.0
1
2020-08-09
3
1.0
1
2020-08-10
4
3.0
1
2020-08-11
5
1.0
1
2020-08-12
6
1.0
1
2020-08-13
7
3.0
1
2020-08-14
8
2.0
1
2020-08-15
9
2.0
1
2020-08-16
10
2.0
1
2020-08-17
11
2.0
1
2020-08-18
12
1.0
1
2020-08-19
13
1.0
1
2020-08-20
14
0.0
1
2020-08-21
15
0.0
1
2020-08-22
16
1.0
1
Obs: I can't simply group the data by cluster because the plot should be sequential. I thought writing a code to identify the number of elements of each cluster sequentially, but then I will face the same problem for plotting. Someone know how to solve this?
The expected result should be something like this (the numbers inside the bar representing the cluster, the x-axis the time in days and the bar width the number of observed days with the same cluster in order :

You could use the dates for the x-axis, the 'constant' column for the y-axis,
and the Cluster id for the coloring.
You can create a custom legend using a list of colored rectangles.
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import pandas as pd
import numpy as np
N = 100
df = pd.DataFrame({'Date': pd.date_range('2020-08-07', periods=N, freq='D'),
'order': np.arange(1, N + 1),
'ClusterNo2': np.random.randint(0, 4, N).astype(float),
'constant': 1})
df['ClusterNo2'] = df['ClusterNo2'].astype(int) # convert to integers
fig, ax = plt.subplots(figsize=(15, 3))
num_clusters = df['ClusterNo2'].max() + 1
colors = plt.cm.Set2.colors
ax.bar(x=range(len(df)), height=df['constant'], width=1, color=[colors[i] for i in df['ClusterNo2']], edgecolor='none')
ax.set_xticks(range(len(df)))
labels = ['' if i % 3 != 0 else day.strftime('%d\n%b %Y') if i == 0 or day.day <= 3 else day.strftime('%d')
for i, day in enumerate(df['Date'])]
ax.set_xticklabels(labels)
ax.margins(x=0, y=0)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
legend_handles = [plt.Rectangle((0, 0), 0, 0, color=colors[i], label=f'{i}') for i in range(num_clusters)]
ax.legend(handles=legend_handles, title='Clusters', bbox_to_anchor=(1.01, 1.01), loc='upper left')
fig.tight_layout()
plt.show()

You could just plot a normal bar graph, with 1 bar corresponding to 1 day. If you make the width also 1, it will look as if the patches are contiguous.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm
# simulate data
total_datapoints = 16
total_clusters = 4
order = np.arange(total_datapoints)
clusters = np.random.randint(0, total_clusters, size=total_datapoints)
# map clusters to colors
cmap = plt.cm.tab10
bounds = np.arange(total_clusters + 1)
norm = BoundaryNorm(bounds, cmap.N)
colors = [cmap(norm(cluster)) for cluster in clusters]
# plot
fig, ax = plt.subplots()
ax.bar(order, np.ones_like(order), width=1, color=colors, align='edge')
# xticks
change_points = np.where(np.diff(clusters) != 0)[0] + 1
change_points = np.unique([0] + change_points.tolist() + [total_datapoints])
ax.set_xticks(change_points)
# annotate clusters
for ii, dx in enumerate(np.diff(change_points)):
xx = change_points[ii] + dx/2
ax.text(xx, 0.5, str(clusters[int(xx)]), ha='center', va='center')
ax.set_xlabel('Time (days)')
plt.show()

How to change pyplot background colour in region of interest?

I have a dataframe with a datetime index:
A B
date
2020-05-04 0 0
2020-05-05 5 0
2020-05-07 2 0
2020-05-09 2 0
2020-05-18 -5 0
2020-05-19 -1 0
2020-05-20 0 0
2020-05-21 1 0
2020-05-22 0 0
2020-05-23 3 0
2020-05-24 1 1
2020-05-25 0 1
2020-05-26 4 1
2020-05-27 3 1
I want to make a lineplot to track A over time and colour the background of the plot red when the values of B are 1. I have implemented this code to make the graph:
from matplotlib import dates as mdates
from matplotlib.colors import ListedColormap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
cmap = ListedColormap(['white','red'])
ax.plot(data['A'])
ax.set_xlabel('')
plt.xticks(rotation = 30)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.pcolorfast(ax.get_xlim(), ax.get_ylim(),
data['B'].values[np.newaxis],
cmap = cmap, alpha = 0.4)
plt.axhline(y = 0, color = 'black')
plt.tight_layout()
This gives me this graph:
But the red region incorrectly starts from 2020-05-21 rather than 2020-05-24 and it doesn't end at the end date in the dataframe. How can I alter my code to fix this?

If you change ax.pcolorfast(ax.get_xlim(), ... by ax.pcolor(data.index, ... you get what you want. The problem with the current code is that by using ax.get_xlim(), it creates a uniform rectangular grid while your index is not uniform (dates are missing), so the coloredmeshed is not like expected. The whole thing is:
from matplotlib import dates as mdates
from matplotlib.colors import ListedColormap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
cmap = ListedColormap(['white','red'])
fig = plt.figure()
ax = fig.add_subplot()
ax.plot(data['A'])
ax.set_xlabel('')
plt.xticks(rotation = 30)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
#here are the two changes use pcolor
ax.pcolor(data.index, #use data.index to create the proper grid
ax.get_ylim(),
data['B'].values[np.newaxis],
cmap = cmap, alpha = 0.4,
linewidth=0, antialiased=True)
plt.axhline(y = 0, color = 'black')
plt.tight_layout()
and you get

I prefer axvspan in this case, see here for more information.
This adaptation will color the areas where data.B==1, including the potential where data.B might not be a continuous block.
With a modified dataframe data from data1.csv (added some more points that are 1):
date A B
5/4/2020 0 0
5/5/2020 5 0
5/7/2020 2 1
5/9/2020 2 1
5/18/2020 -5 0
5/19/2020 -1 0
5/20/2020 0 0
5/21/2020 1 0
5/22/2020 0 0
5/23/2020 3 0
5/24/2020 1 1
5/25/2020 0 1
5/26/2020 4 1
5/27/2020 3 1
from matplotlib import dates as mdates
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('data1.csv',index_col='date')
data.index = pd.to_datetime(data.index)
fig = plt.figure()
ax = fig.add_subplot()
ax.plot(data['A'])
plt.xticks(rotation = 30)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.axhline(y = 0, color = 'black')
# in this case I'm looking for a pair of ones to determine where to color
for i in range(1,len(data.B)):
if data.B[i]==True and data.B[i-1]==True:
plt.axvspan(data.index[i-1], data.index[i], color='r', alpha=0.4, lw=0)
plt.tight_layout()
If data.B==1 will always be "one block" you can do away with the for loop and just use something like this in its place:
first = min(idx for idx, val in enumerate(data.B) if val == 1)
last = max(idx for idx, val in enumerate(data.B) if val == 1)
plt.axvspan(data.index[first], data.index[last], color='r', alpha=0.4, lw=0)
Regarding "why" your data does not align, #Ben.T has this solution.
UPDATE: as pointed out, the for loop could be too crude for large datasets. The following uses numpy to find the falling and rising edges of data.B and then loops on those results:
import numpy as np
diffB = np.append([0], np.diff(data.B))
up = np.where(diffB == 1)[0]
dn = np.where(diffB == -1)[0]
if diffB[np.argmax(diffB!=0)]==-1:
# we have a falling edge before rising edge, must have started 'up'
up = np.append([0], up)
if diffB[len(diffB) - np.argmax(diffB[::-1]) - 1]==1:
# we have a rising edge that never fell, force it 'dn'
dn = np.append(dn, [len(data.B)-1])
for i in range(len(up)):
plt.axvspan(data.index[up[i]], data.index[dn[i]], color='r', alpha=0.4, lw=0)

labelling bins in each subplots of an histogram chart

I have a dataframe,df with 29 rows by 24 columns dimension
Index 0.0 5.0 34.0 ... 22.0
2017-08-03 00:00:00 10 0 10 0
2017-08-04 00:00:00 20 60 1470 20
2017-08-05 00:00:00 0 58 0 24
2017-08-06 00:00:00 0 0 480 24
2017-09-07 00:00:00 0 0 0 25
: : : : :
: : : : :
2017-09-30 00:00:00
I intend to label bins for each subplot representing a column in the histogram chart.I have been able to draw the histogram in each subplot for each column using this code
fig = plt.figure(figsize = (15,20))
ax = fig.gca()
#Initialize the figure
plt.style.use('seaborn-darkgrid')
df.hist(ax = ax)
However, the labels of the bins of each subplot are far apart and bin labels are not explicitly specified by ranges on the x-axis which is difficult to interpret. I have looked at
Aligning bins to xticks in plt.hist but it doesnt explicitly solve for labelling bins when subplots are concerned. Any help will be great...
I have also tried this but i get ValueError: too many values to unpack (expected 2)
x=[0,40,80,120,160,200,240,280,320]
fig = plt.figure(figsize = (15,20))
ax = fig.gca()
# Initialize the figure
plt.style.use('seaborn-darkgrid')
n,bins= plt.hist(df,bins= x)
#labels & axes
plt.locator_params(nbins=8, axis='x')
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
plt.title('Daily occurrence',fontsize=16)
plt.xlabel('Number of occurrence',fontsize=12)
plt.ylabel('Frequency',fontsize=12)
plt.xticks(x)
plt.xlim(0,320)

Python error: generating a scatter plot using matplotlib

I am a python newbie suffering from how to import CSV file in matplotlib.pyplot
I would like to see the relationship between hour (=how many hours people spent to play a video game) and level (=game level). and then I would like to draw a scatter plot with Tax in different colors between female(1) and male(0).So, my x would be 'hour' and my y would be 'level'.
my data csv file looks like this:
hour gender level
0 8 1 20.00
1 9 1 24.95
2 12 0 10.67
3 12 0 18.00
4 12 0 17.50
5 13 0 13.07
6 10 0 14.45
...
...
499 12 1 19.47
500 16 0 13.28
Here's my code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df=pd.read_csv('data.csv')
plt.plot(x,y, lavel='some relationship')
plt.title("Some relationship")
plt.xlabel('hour')
plt.ylabel('level')
plt.plot[gender(gender=1), '-b', label=female]
plt.plot[gender(gender=0), 'gD', label=male]
plt.axs()
plt.show()
I would like to draw the following graph. So, there will be two lines of male and female.
y=level| #----->male
| #
| * *----->female
|________________ x=hour
However, I am not sure how to solve this problem.
I kept getting an error NameError: name 'hour' is not defined.

Could do it in this way:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(data={"hour": [8,9,12,12,12,13,10],
"gender": [1,1,0,0,0,0,0],
"level": [20, 24.95, 10.67, 18, 17.5, 13.07, 14.45]})
df.sort_values("hour", ascending=True, inplace=True)
fig = plt.figure(dpi=80)
ax = fig.add_subplot(111, aspect='equal')
ax.plot(df.hour[df.gender==1], df.level[df.gender==1], c="red", label="male")
ax.plot(df.hour[df.gender==0], df.level[df.gender==0], c="blue", label="female")
plt.xlabel('hour')
plt.ylabel('level')

Pandas dataframe plotting - issue when switching from two subplots to single plot w/ secondary axis

I have two sets of data I want to plot together on a single figure. I have a set of flow data at 15 minute intervals I want to plot as a line plot, and a set of precipitation data at hourly intervals, which I am resampling to a daily time step and plotting as a bar plot. Here is what the format of the data looks like:
2016-06-01 00:00:00 56.8
2016-06-01 00:15:00 52.1
2016-06-01 00:30:00 44.0
2016-06-01 00:45:00 43.6
2016-06-01 01:00:00 34.3
At first I set this up as two subplots, with precipitation and flow rate on different axis. This works totally fine. Here's my code:
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime
filename = 'manhole_B.csv'
plotname = 'SSMH-2A B'
plt.style.use('bmh')
# Read csv with precipitation data, change index to datetime object
pdf = pd.read_csv('precip.csv', delimiter=',', header=None, index_col=0)
pdf.columns = ['Precipitation[in]']
pdf.index.name = ''
pdf.index = pd.to_datetime(pdf.index)
pdf = pdf.resample('D').sum()
print(pdf.head())
# Read csv with flow data, change index to datetime object
qdf = pd.read_csv(filename, delimiter=',', header=None, index_col=0)
qdf.columns = ['Flow rate [gpm]']
qdf.index.name = ''
qdf.index = pd.to_datetime(qdf.index)
# Plot
f, ax = plt.subplots(2)
qdf.plot(ax=ax[1], rot=30)
pdf.plot(ax=ax[0], kind='bar', color='r', rot=30, width=1)
ax[0].get_xaxis().set_ticks([])
ax[1].set_ylabel('Flow Rate [gpm]')
ax[0].set_ylabel('Precipitation [in]')
ax[0].set_title(plotname)
f.set_facecolor('white')
f.tight_layout()
plt.show()
2 Axis Plot
However, I decided I want to show everything on a single axis, so I modified my code to put precipitation on a secondary axis. Now my flow data data has disppeared from the plot, and even when I set the axis ticks to an empty set, I get these 00:15 00:30 and 00:45 tick marks along the x-axis.
Secondary-y axis plots
Any ideas why this might be occuring?
Here is my code for the single axis plot:
f, ax = plt.subplots()
qdf.plot(ax=ax, rot=30)
pdf.plot(ax=ax, kind='bar', color='r', rot=30, secondary_y=True)
ax.get_xaxis().set_ticks([])

Here is an example:
Setup
In [1]: from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
df = pd.DataFrame({'x' : np.arange(10),
'y1' : np.random.rand(10,),
'y2' : np.square(np.arange(10))})
df
Out[1]: x y1 y2
0 0 0.451314 0
1 1 0.321124 1
2 2 0.050852 4
3 3 0.731084 9
4 4 0.689950 16
5 5 0.581768 25
6 6 0.962147 36
7 7 0.743512 49
8 8 0.993304 64
9 9 0.666703 81
Plot
In [2]: fig, ax1 = plt.subplots()
ax1.plot(df['x'], df['y1'], 'b-')
ax1.set_xlabel('Series')
ax1.set_ylabel('Random', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx() # Note twinx, not twiny. I was wrong when I commented on your question.
ax2.plot(df['x'], df['y2'], 'ro')
ax2.set_ylabel('Square', color='r')
for tl in ax2.get_yticklabels():
tl.set_color('r')
Out[2]: