We Keep Coding

Plotting two pandas series together one appears flat

I am practicing with Python Pandas plotting functions and I am trying to plot the content of two series extracted from the same dataframe into one plot.
When I plot the two series individually the result is correct. However, when I plot them together, the one that I plot as second appears flat in the picture.
Here is my code:
# dailyFlow and smooth are created in the same way from the same dataframe
dailyFlow = pd.Series(dataFrame...
smooth = pd.Series(dataFrame...
# lower the noise in the signal with standard deviation = 6
smooth = smooth.resample('D').sum().rolling(31, center=True, win_type='gaussian').sum(std=6)
dailyFlow.plot(style ='-b')
plt.legend(loc = 'upper right')
plt.show()
smooth.plot(style ='-r')
plt.legend(loc = 'upper right')
plt.show()
plt.figure(figsize=(12,5))
smooth.plot(style ='-r')
dailyFlow.plot(style ='-b')
plt.legend(loc = 'upper right')
plt.show()
Here is the output of my function:
I already tried using the parameter secondary_y=True in the second plot, but then I lose the information on the second line in the legend and the scaling between the two plots is wrong.
Many sources on the Internet seem to suggest that plotting the two series like I am doing should be correct, but then why is the third plot incorrect?
Thank you very much for your help.

For the data you have, the 3rd plot is correct. Look at the scale of the y axis on your two plots: one goes up to 70,000 and the other to 60,000,000.
I suspect what you actually want is a .rolling(...).mean() which should have a range comparable to your original data.

If you would like to make both plots bigger, you cold try something like this
fig, ax1 = plt.subplots()
ax1.set_ylim([0, 75000])
# plot first graph
ax2 = ax1.twinx() # second axes that shares the same x-axis
ax2.set_ylim([0, 60000000])
#plot the second graph

Matplotlib showing X axis labels out of order but based on their values

I am trying to have my graph show the x labels in the order of the below. But its putting them in the graph based on their values.
Can someone advise how to force the plot to show them in the below order?
How I want the X-axis to be ordered:
'TD1BALMO','TD1CURMON','TD1+1_M', 'TD1+2_M', 'TD1+3_M', 'TD1+4_M', 'TD1+5_M'
how it is being ordered (I don't know why)
'TD1+1_M', 'TD1+2_M', 'TD1+3_M', 'TD1+4_M', 'TD1+5_M', 'TD1BALMO','TD1CURMON'
Assign routes for 3.30.2020 to a variable:
TD3_FFA_Months = FFA_dirty_rates[FFA_dirty_rates.RouteIdentifier.isin(['TD1BALMO','TD1CURMON','TD1+1_M', 'TD1+2_M', 'TD1+3_M', 'TD1+4_M', 'TD1+5_M']) & (FFA_dirty_rates.ArchiveDate == '2020-03-30')]
Visualizing the data
fig = plt.figure(figsize=(10, 8))
plt.plot('RouteIdentifier', 'RouteAverage', data=TD3_FFA_Months, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)
plt.xlabel('Forward Months')
plt.ylabel('WS Rates')
plt.title('Rates over months and years')
plt.show()
I am having trouble putting in a picture but the chart is showing the X axis in the below order. Notice the TD1BALMO and TD1CURMO were placed at the end of the x axis.
'TD1+1_M', 'TD1+2_M', 'TD1+3_M', 'TD1+4_M', 'TD1+5_M', 'TD1BALMO','TD1CURMON'

Add an additional axis with different ticks in matplotlib

I have a plot that plots iteration vs. progress for an optimization problem. What I want to do is add an additional axis (at the top of the plot) that uses the same data - but also marks wall time. Thus there are two x-axes in 1-to-1 correspondence with each other, on top and bottom, and one data series. I've created the second axis as:
ax2 = ax.twiny()
ax2.set_xlabel('Wall Time (s)')
But now I don't know how to add the new ticks. I'm alternatively open to having two x-data series for each y series, but I don't know how to do this either.

I figured it out:
ax2 = ax.twiny()
ax2.set_xlabel('Wall Time (s)')
ax2.set_xlim(0.0, np.max(all_data, axis=0)[0] * scale_amt)

Plotting a variable number of series and data forecasts onto subplots, including axis labels, formatting and line colours

I'm writing a program which gets data and then uses time series forecasting to predict data values for the next, say, 300 data points.
However, only data which fulfills a certain condition will be plotted, so there is no defined number of subplots for the add_subplot() method. I'm aware of the plot.subplots() function, but something such as
fig, (ax1, ax2) = plt.subplots(1, 2)
implies that two graphs will definitely be plotted and I need to change the specific amount, like with a parameter.
Here is a simplified version of the current code which results in each plot being in separate windows:
fig = plt.figure() # creates a figure instance for the final graph output
plots = 1 # indicates the total number of plots to plot, starting from 1
# passed as a parameter to the add_subplot() function
for data in dataSet:
forecast(data, fig, plots)
plt.figure(fig.number)
plt.show()
And the function:
import matplotlib.pyplot as plt
import matplotlib.ticker as tick
from pandas import Series
from statsmodels.tsa.holtwinters import ExponentialSmoothing
def forecast(data, superFigure, plotNumber):
index = range(0, len(data))
plotData = Series(data, index)
# fit the data values into a specific model:
modelFit = ExponentialSmoothing(plotData, trend="add").fit()
# forecast for the next 300 points:
modelForecast = modelFit.forecast(300)
if [condition]:
# plot the original data points:
points = plotData.plot(marker='x', color='black', label='Base Data')
points.set_xlim(0, len(data) + 300)
# plot the forecast in a different colour:
modelForecast.plot(marker='x', ax=points, color='blue', label='Forecasted Data')
plt.title("Plot Title")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
# format the axes, adding thousand separator
points.get_xaxis().set_major_formatter(
tick.FuncFormatter(lambda x, p: format(int(x), ',')))
points.get_yaxis().set_major_formatter(
tick.FuncFormatter(lambda x, p: format(int(x), ',')))
plt.legend()
plt.show()
This produces multiple graphs such as this (actual labels have been cut out).
Unfortunately you have to close each graph before viewing the next one, and I want every graph to be visible on one page.
I tried changing the code within the "if [condition]" to:
if [condition]:
points = plotData.plot(marker='x', color='black', label='Base Data')
modelForecast.plot(marker='x', ax=points, color='blue', label='Forecasted Data')
dataLine = plt.gca().get_lines()[0]
forecastLine = plt.gca().get_lines()[1]
# put all x and y values into single lists by concatenating them
totalXData = [*dataLine.get_xdata(), *forecastLine.get_xdata()]
totalYData = [*dataLine.get_ydata(), *forecastLine.get_ydata()]
subset = superFigure.add_subplot(10, 10, plotNumber)
for i in range(0, len(totalXData)):
subset.plot(totalXData[i], totalYData[i])
plotNumber += 1
These changes produce this exact graph which seems to have the other graphs squished in the top-left corner, and I get "MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance" warnings.
If I change "superFigure.add_subplot(10, 10, plotNumber)" to "superFigure.add_subplot(20, 20, plotNumber)" I also get "UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations".
I then tried to change it to:
if [condition]:
fig, ax = plt.subplots()
plotData.plot(marker='x', ax=ax, color='black', label='Base Data')
modelForecast.plot(marker='x', ax=ax, color='blue', label='Forecasted Data')
ax.set([...])
ax.legend()
plt.show()
which doesn't produce the desired output assumedly because it recreates the figure on each call of forecast(), unless a figure window can contain multiple figures.
I also sometimes get the following warning:
RuntimeWarning: More than 20 figures have been opened. Figures created
through the pyplot interface (matplotlib.pyplot.figure) are retained
until explicitly closed and may consume too much memory.
fig, ax = plt.subplots()
How can I create subplots which include all the formatting and are displayed in one window all together?

matplotlib: Creating two (stacked) subplots with SHARED X axis but SEPARATE Y axis values

I am using matplotlib 1.2.x and Python 2.6.5 on Ubuntu 10.0.4. I am trying to create a SINGLE plot that consists of a top plot and a bottom plot.
The X axis is the date of the time series. The top plot contains a candlestick plot of the data, and the bottom plot should consist of a bar type plot - with its own Y axis (also on the left - same as the top plot). These two plots should NOT OVERLAP.
Here is a snippet of what I have done so far.
datafile = r'/var/tmp/trz12.csv'
r = mlab.csv2rec(datafile, delimiter=',', names=('dt', 'op', 'hi', 'lo', 'cl', 'vol', 'oi'))
mask = (r["dt"] >= datetime.date(startdate)) & (r["dt"] <= datetime.date(enddate))
selected = r[mask]
plotdata = zip(date2num(selected['dt']), selected['op'], selected['cl'], selected['hi'], selected['lo'], selected['vol'], selected['oi'])
# Setup charting
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # Eg, Jan 12
dayFormatter = DateFormatter('%d') # Eg, 12
monthFormatter = DateFormatter('%b %y')
# every Nth month
months = MonthLocator(range(1,13), bymonthday=1, interval=1)
fig = pylab.figure()
fig.subplots_adjust(bottom=0.1)
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(months)#mondays
ax.xaxis.set_major_formatter(monthFormatter) #weekFormatter
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.format_ydata = price
ax.grid(True)
candlestick(ax, plotdata, width=0.5, colorup='g', colordown='r', alpha=0.85)
ax.xaxis_date()
ax.autoscale_view()
pylab.setp( pylab.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# Add volume data
# Note: the code below OVERWRITES the bottom part of the first plot
# it should be plotted UNDERNEATH the first plot - but somehow, that's not happening
fig.subplots_adjust(hspace=0.15)
ay = fig.add_subplot(212)
volumes = [ x[-2] for x in plotdata]
ay.bar(range(len(plotdata)), volumes, 0.05)
pylab.show()
I have managed to display the two plots using the code above, however, there are two problems with the bottom plot:
It COMPLETELY OVERWRITES the bottom part of the first (top) plot - almost as though the second plot was drawing on the same 'canvas' as the first plot - I can't see where/why that is happening.
It OVERWRITES the existing X axis with its own indice, the X axis values (dates) should be SHARED between the two plots.
What am I doing wrong in my code?. Can someone spot what is causing the 2nd (bottom) plot to overwrite the first (top) plot - and how can I fix this?
Here is a screenshot of the plot created by the code above:
[[Edit]]
After modifying the code as suggested by hwlau, this is the new plot. It is better than the first in that the two plots are separate, however the following issues remain:
The X axis should be SHARED by the two plots (i.e. the X axis should be shown only for the 2nd [bottom] plot)
The Y values for the 2nd plot seem to be formmated incorrectly
I think these issues should be quite easy to resolve however, my matplotlib fu is not great at the moment, as I have only recently started programming with matplotlib. any help will be much appreciated.

There seem to be a couple of problems with your code:
If you were using figure.add_subplots with the full
signature of subplot(nrows, ncols, plotNum) it may have
been more apparent that your first plot asking for 1 row
and 1 column and the second plot was asking for 2 rows and
1 column. Hence your first plot is filling the whole figure.
Rather than fig.add_subplot(111) followed by fig.add_subplot(212)
use fig.add_subplot(211) followed by fig.add_subplot(212).
Sharing an axis should be done in the add_subplot command using sharex=first_axis_instance
I have put together an example which you should be able to run:
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import datetime as dt
n_pts = 10
dates = [dt.datetime.now() + dt.timedelta(days=i) for i in range(n_pts)]
ax1 = plt.subplot(2, 1, 1)
ax1.plot(dates, range(10))
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
ax2.bar(dates, range(10, 20))
# Now format the x axis. This *MUST* be done after all sharex commands are run.
# put no more than 10 ticks on the date axis.
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
# format the date in our own way.
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
# rotate the labels on both date axes
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(30)
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(30)
# tweak the subplot spacing to fit the rotated labels correctly
plt.subplots_adjust(hspace=0.35, bottom=0.125)
plt.show()
Hope that helps.

You should change this line:
ax = fig.add_subplot(111)
to
ax = fig.add_subplot(211)
The original command means that there is one row and one column so it occupies the whole graph. So your second graph fig.add_subplot(212) cover the lower part of the first graph.
Edit
If you dont want the gap between two plots, use subplots_adjust() to change the size of the subplots margin.

The example from #Pelson, simplified.
import matplotlib.pyplot as plt
import datetime as dt
#Two subplots that share one x axis
fig,ax=plt.subplots(2,sharex=True)
#plot data
n_pts = 10
dates = [dt.datetime.now() + dt.timedelta(days=i) for i in range(n_pts)]
ax[0].bar(dates, range(10, 20))
ax[1].plot(dates, range(10))
#rotate and format the dates on the x axis
fig.autofmt_xdate()
The subplots sharing an x-axis are created in one line, which is convenient when you want more than two subplots:
fig, ax = plt.subplots(number_of_subplots, sharex=True)
To format the date correctly on the x axis, we can simply use fig.autofmt_xdate()
For additional informations, see shared axis demo and date demo from the pylab examples.
This example ran on Python3, matplotlib 1.5.1