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how to set x_axis label(not xtick label) for all subplots in relplot?

I tried drawing subplot through relplot method of seaborn. Now the question is, due to the original dataset is varying, sometimes I don't know how much final subplots will be.
I set col_wrap to limit it, but sometimes the results looks not so good. For example, I set col_wrap = 3, while there are 5 subplots as below:
As the figure shows, the x_axis only occurs in the C D E, which seems strange. I want x axis label is shown in all subplots(from A to E).
Now I already know that facet_kws={'sharex': 'col'} allows plots to have independent axis scales(according to set axis limits on individual facets of seaborn facetgrid).
But I want set labels for x axis of all subplots.I haven't found any solution for it.
Any keyword like set_xlabels in object FacetGrid seems to be useless, because official document announces they only control "on the bottom row of the grid".
FacetGrid.set_xlabels(label=None, clear_inner=True, **kwargs)
Label the x axis on the bottom row of the grid.
The following are my example data and my code:
city date value
0 A 1 9
1 B 1 20
2 C 1 4
3 D 1 33
4 E 1 2
5 A 2 22
6 B 2 32
7 C 2 27
8 D 2 32
9 E 2 18
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_excel("data/example_data.xlsx")
# print(df)
g = sns.relplot(data=df, x="date", y="value", kind="line", col="city", col_wrap=3,
errorbar=None, facet_kws={'sharex': 'col'})
(g.set_axis_labels("x_axis", "y_axis", )
.set_titles("{col_name}")
.tight_layout()
.add_legend()
)
plt.subplots_adjust(top=0.94, wspace=None, hspace=0.4)
plt.show()
Thanks in advance.

In order to reduce superfluous information, Seaborn makes these inner labels invisible. You can make them visible again:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.DataFrame({'date': np.repeat([1, 2], 5),
'value': np.random.randint(1, 20, 10),
'city': np.tile([*'abcde'], 2)})
# print(df)
g = sns.relplot(data=df, x="date", y="value", kind="line", col="city", col_wrap=3,
errorbar=None, facet_kws={'sharex': 'col'})
g.set_titles("{col_name}")
g.add_legend()
for ax in g.axes.flat:
ax.set_xlabel('x axis', visible=True)
ax.set_ylabel('y axis', visible=True)
plt.subplots_adjust(top=0.94, wspace=None, hspace=0.4)
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)

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')

Timeseries plot from CSV data (Timestamp and events): x-label constant

(This question can be read alone, but is a sequel to: Timeseries from CSV data (Timestamp and events))
I would like to visualize CSV data (from 2 files) as shown below, by a timeseries representation, using python's pandas module (see links below).
Sample data of df1:
TIMESTAMP eventid
0 2017-03-20 02:38:24 1
1 2017-03-21 05:59:41 1
2 2017-03-23 12:59:58 1
3 2017-03-24 01:00:07 1
4 2017-03-27 03:00:13 1
The 'eventid' column always contains the value of 1, and I am trying to show the sum of events for each day in the dataset.
The 2nd dataset, df0, has similar structure but contains only zeros:
Sample data of df0:
TIMESTAMP eventid
0 2017-03-21 01:38:24 0
1 2017-03-21 03:59:41 0
2 2017-03-22 11:59:58 0
3 2017-03-24 01:03:07 0
4 2017-03-26 03:50:13 0
The x-axis label only shows the same date, and my question is: How can the different dates be shown? (What causes the same date to be shown multiple times on x labels?)
script so far:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
df1 = pd.read_csv('timestamp01.csv', parse_dates=True, index_col='TIMESTAMP')
df0 = pd.read_csv('timestamp00.csv', parse_dates=True, index_col='TIMESTAMP')
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(df0.resample('D').size())
ax1.set_xlim([pd.to_datetime('2017-01-27'), pd.to_datetime('2017-04-30')])
ax1.xaxis.set_major_formatter(ticker.FixedFormatter
(df0.index.strftime('%Y-%m-%d')))
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=15)
ax2.plot(df1.resample('D').size())
ax2.set_xlim([pd.to_datetime('2017-03-22'), pd.to_datetime('2017-04-29')])
ax2.xaxis.set_major_formatter(ticker.FixedFormatter(df1.index.strftime
('%Y-%m-%d')))
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=15)
plt.show()
Output: (https://www.dropbox.com/s/z21koflkzglm6c3/figure_1.png?dl=0)
Links I have tried to follow:
http://pandas.pydata.org/pandas-docs/stable/visualization.html
Multiple timeseries plots from Pandas Dataframe
Pandas timeseries plot setting x-axis major and minor ticks and labels
Any help is much appreciated.

Making the example reproducible, we can create the following text file (data/timestamp01.csv):
TIMESTAMP;eventid
2017-03-20 02:38:24;1
2017-03-21 05:59:41;1
2017-03-23 12:59:58;1
2017-03-24 01:00:07;1
2017-03-27 03:00:13;1
(same for data/timestamp00.csv). We can then read them in
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
df1 = pd.read_csv('data/timestamp01.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")
df0 = pd.read_csv('data/timestamp00.csv', parse_dates=True, index_col='TIMESTAMP', sep=";")
Plotting them
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(df0.resample('D').size())
ax2.plot(df1.resample('D').size())
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=30, ha="right")
plt.show()
results in
which is the desired plot.

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]: