I am unable to show a bar and line graph on the same plot. Example code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Df = pd.DataFrame(data=np.random.randn(10,4), index=pd.DatetimeIndex(start='2005', freq='M', periods=10), columns=['A','B','C','D'])
fig = plt.figure()
ax = fig.add_subplot(111)
Df[['A','B']].plot(kind='bar', ax=ax)
Df[['C','D']].plot(ax=ax, color=['r', 'c'])
You can also try this:
fig = plt.figure()
ax = DF['A','B'].plot(kind="bar");plt.xticks(rotation=0)
ax2 = ax.twinx()
ax2.plot(ax.get_xticks(),DF['C','D'],marker='o')
I wanted to know as well, however all existing answers are not for showing bar and line graph on the same plot, but on different axis instead.
so I looked for the answer myself and have found an example that is working -- Plot Pandas DataFrame as Bar and Line on the same one chart. I can confirm that it works.
What baffled me was that, the almost same code works there but does not work here. I.e., I copied the OP's code and can verify that it is not working as expected.
The only thing I could think of is to add the index column to Df[['A','B']] and Df[['C','D']], but I don't know how since the index column doesn't have a name for me to add.
Today, I realize that even I can make it works, the real problem is that Df[['A','B']] gives a grouped (clustered) bar chart, but grouped (clustered) line chart is not supported.
The issue is that the pandas bar plot function treats the dates as a categorical variable where each date is considered to be a unique category, so the x-axis units are set to integers starting at 0 (like the default DataFrame index when none is assigned).
The pandas line plot uses x-axis units corresponding to the DatetimeIndex, for which 0 is located on January 1970 and the integers count the number of periods (months in this example) since then. So let's take a look at what happens in this particular case:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
# Create random data
rng = np.random.default_rng(seed=1) # random number generator
df = pd.DataFrame(data=rng.normal(size=(10,4)),
index=pd.date_range(start='2005', freq='M', periods=10),
columns=['A','B','C','D'])
# Create a pandas bar chart overlaid with a pandas line plot using the same
# Axes: note that seeing as I do not set any variable for x, df.index is used
# by default, which is usually what we want when dealing with a dataset
# containing a time series
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax);
The bars are nowhere to be seen. If you check what x ticks are being used, you'll see that the single major tick placed on January is 420 followed by these minor ticks for the other months:
ax.get_xticks(minor=True)
# [421, 422, 423, 424, 425, 426, 427, 428, 429]
This is because there are 35 years * 12 months since 1970, the numbering starts at 0 so January 2005 lands on 420. This explains why we do not see the bars. If you change the x-axis limit to start from zero, here is what you get:
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax)
ax.set_xlim(0);
The bars are squashed to the left, starting on January 1970. This problem can be solved by setting use_index=False in the line plot function so that the lines also start at 0:
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax, use_index=False)
ax.set_xticklabels(df.index.strftime('%b'), rotation=0, ha='center');
# # Optional: move legend to new position
# import matplotlib.pyplot as plt # v 3.3.2
# ax.legend().remove()
# plt.gcf().legend(loc=(0.08, 0.14));
In case you want more advanced tick label formatting, you can check out the answers to this question which are compatible with this example. If you need more flexible/automated tick label formatting as provided by the tick locators and formatters in the matplotlib.dates module, the easiest is to create the plot with matplotlib like in this answer.
You can do something like that, both on the same figure:
In [4]: Df = pd.DataFrame(data=np.random.randn(10,4), index=pd.DatetimeIndex(start='2005', freq='M', periods=10), columns=['A','B','C','D'])
In [5]: fig, ax = plt.subplots(2, 1) # you can pass sharex=True, sharey=True if you want to share axes.
In [6]: Df[['A','B']].plot(kind='bar', ax=ax[0])
Out[6]: <matplotlib.axes.AxesSubplot at 0x10cf011d0>
In [7]: Df[['C','D']].plot(color=['r', 'c'], ax=ax[1])
Out[7]: <matplotlib.axes.AxesSubplot at 0x10a656ed0>
Related
My data is in a dataframe of two columns: y and x. The data refers to the past few years. Dummy data is below:
np.random.seed(167)
rng = pd.date_range('2017-04-03', periods=365*3)
df = pd.DataFrame(
{"y": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365*3)]),
"x": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365*3)])
}, index=rng
)
In first attempt, I plotted a scatterplot with Seaborn using the following code:
import seaborn as sns
import matplotlib.pyplot as plt
def plot_scatter(data, title, figsize):
fig, ax = plt.subplots(figsize=figsize)
ax.set_title(title)
sns.scatterplot(data=data,
x=data['x'],
y=data['y'])
plot_scatter(data=df, title='dummy title', figsize=(10,7))
However, I would like to generate a 4x3 matrix including 12 scatterplots, one for each month with year as hue. I thought I could create a third column in my dataframe that tells me the year and I tried the following:
import seaborn as sns
import matplotlib.pyplot as plt
def plot_scatter(data, title, figsize):
fig, ax = plt.subplots(figsize=figsize)
ax.set_title(title)
sns.scatterplot(data=data,
x=data['x'],
y=data['y'],
hue=data.iloc[:, 2])
df['year'] = df.index.year
plot_scatter(data=df, title='dummy title', figsize=(10,7))
While this allows me to see the years, it still shows all the data in the same scatterplot instead of creating multiple scatterplots, one for each month, so it's not offering the level of detail I need.
I could slice the data by month and build a for loop that plots one scatterplot per month but I actually want a matrix where all the scatterplots use similar axis scales. Does anyone know an efficient way to achieve that?
To create multiple subplots at once, seaborn introduces figure-level functions. The col= argument indicates which column of the dataframe should be used to identify the subplots. col_wrap= can be used to tell how many subplots go next to each other before starting an additional row.
Note that you shouldn't create a figure, as the function creates its own new figure. It uses the height= and aspect= arguments to tell the size of the individual subplots.
The code below uses a sns.relplot() on the months. An extra column for the months is created; it is made categorical to fix an order.
To remove the month= in the title, you can loop through the generated axes (a recent seaborn version is needed for axes_dict). With sns.set(font_scale=...) you can change the default sizes of all texts.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(167)
dates = pd.date_range('2017-04-03', periods=365 * 3, freq='D')
df = pd.DataFrame({"y": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365 * 3)]),
"x": np.cumsum([np.random.uniform(-0.01, 0.01) for _ in range(365 * 3)])
}, index=dates)
df['year'] = df.index.year
month_names = pd.date_range('2017-01-01', periods=12, freq='M').strftime('%B')
df['month'] = pd.Categorical.from_codes(df.index.month - 1, month_names)
sns.set(font_scale=1.7)
g = sns.relplot(kind='scatter', data=df, x='x', y='y', hue='year', col='month', col_wrap=4, height=4, aspect=1)
# optionally remove the `month=` in the title
for name, ax in g.axes_dict.items():
ax.set_title(name)
plt.setp(g.axes, xlabel='', ylabel='') # remove all x and y labels
g.axes[-2].set_xlabel('x', loc='left') # set an x label at the left of the second to last subplot
g.axes[4].set_ylabel('y') # set a y label to 5th subplot
plt.subplots_adjust(left=0.06, bottom=0.06) # set some more spacing at the left and bottom
plt.show()
I am trying to create a heat map from pandas dataframe using seaborn library. Here, is the code:
test_df = pd.DataFrame(np.random.randn(367, 5),
index = pd.DatetimeIndex(start='01-01-2000', end='01-01-2001', freq='1D'))
ax = sns.heatmap(test_df.T)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%d'))
However, I am getting a figure with nothing printed on the x-axis.
Seaborn heatmap is a categorical plot. It scales from 0 to number of columns - 1, in this case from 0 to 366. The datetime locators and formatters expect values as dates (or more precisely, numbers that correspond to dates). For the year in question that would be numbers between 730120 (= 01-01-2000) and 730486 (= 01-01-2001).
So in order to be able to use matplotlib.dates formatters and locators, you would need to convert your dataframe index to datetime objects first. You can then not use a heatmap, but a plot that allows for numerical axes, e.g. an imshow plot. You may then set the extent of that imshow plot to correspond to the date range you want to show.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
df = pd.DataFrame(np.random.randn(367, 5),
index = pd.DatetimeIndex(start='01-01-2000', end='01-01-2001', freq='1D'))
dates = df.index.to_pydatetime()
dnum = mdates.date2num(dates)
start = dnum[0] - (dnum[1]-dnum[0])/2.
stop = dnum[-1] + (dnum[1]-dnum[0])/2.
extent = [start, stop, -0.5, len(df.columns)-0.5]
fig, ax = plt.subplots()
im = ax.imshow(df.T.values, extent=extent, aspect="auto")
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
fig.colorbar(im)
plt.show()
I found this question when trying to do a similar thing and you can hack together a solution but it's not very pretty.
For example I get the current labels, loop over them to find the ones for January and set those to just the year, setting the rest to be blank.
This gives me year labels in the correct position.
xticklabels = ax.get_xticklabels()
for label in xticklabels:
text = label.get_text()
if text[5:7] == '01':
label.set_text(text[0:4])
else:
label.set_text('')
ax.set_xticklabels(xticklabels)
Hopefully from that you can figure out what you want to do.
I am trying to plot a simple .csv file downloaded from Yahoo-finance (file example here), but I cannot understand why the years appear as (apparently) random numbers. Please see image below:
Another thing that I would like to do is to remove the x axis from the top graph (since the same axis is already in the bottom plot) but I would like to keep the dashed grid. I tired to use ax[0].set_xticklabels([]), but it didn't work.
Here is my code:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MonthLocator, YearLocator
#LOAD DATA
df_name = "0P0000UL8U.L.csv"
col_list = ["Date", "Adj Close"] #list of column to import
df = pd.read_csv(df_name, header=0, usecols=col_list, na_values=['null'], thousands=r',', parse_dates=["Date"], dayfirst=True)
df = df.dropna() #Drop the rows where at least one element is missing.
df.set_index("Date", inplace = True)
df.index = [pd.to_datetime(date).date() for date in df.index] #convert index to datetime.date, not datetime.datetime.
print("Opening df:\n", df)
print("\nLength of df: ", len(df.index))
#PLOT DATA
fig, ax = plt.subplots(2,1, figsize=(11,5))
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.25, hspace=0.8) #Adjust space between graphs
df[["Adj Close"]].plot(ax=ax[0], kind="line", style="-", color="blue", stacked=False, rot=90)
ax[0].set_axisbelow(True) # To put plot grid below plots
ax[0].yaxis.grid(color='gray', linestyle='dashed')
ax[0].xaxis.grid(color='gray', linestyle='dashed')
ax[0].xaxis.set_major_locator(YearLocator()) # specify a MonthLocator
ax[0].xaxis.set_major_formatter(DateFormatter("%b %Y"))
ax[0].set(xlabel=None, ylabel="Price") # Set title and labels for axes
df[["Adj Close"]].plot(ax=ax[1], kind="line", style="-", color="blue", stacked=False, rot=90)
ax[1].set_axisbelow(True) # To put plot grid below plots
ax[1].yaxis.grid(color='gray', linestyle='dashed')
ax[1].xaxis.grid(color='gray', linestyle='dashed')
ax[1].xaxis.set_major_locator(YearLocator()) # specify a MonthLocator
ax[1].xaxis.set_major_formatter(DateFormatter("%b %Y"))
ax[1].set(xlabel="Time", ylabel="Price") # Set title and labels for axes
fig.savefig("0P0000UL8U.L.png", bbox_inches='tight', dpi=300)
What am I doing wrong? Thank for any help in advance.
To remove the x-Axis labels from the top graph, you can add the following line:
ax[0].tick_params(labelbottom=False)
before ax[0].set(xlabel=None, ylabel="Price")
It's not your fault. Python 3 is very far from stable yet. That's why hardcore developers still prefer Python 2. This time matplotlib devs screwed dates handling. They even have a number of corresponding bugs (#18010, #17983, #34850).
Meantime you can downgrade matplotlib to v 3.2.2, it's working perfectly and wait if devs repair the bug.
I am trying to do analysis on a bike share dataset. Part of the analysis includes showing the weekends' demand in date wise plot.
My dataframe in pandas with last 5 row looks like this.
Here is my code for date vs total ride plot.
import seaborn as sns
sns.set_style("darkgrid")
plt.plot(d17_day_count)
plt.show()
.
I want to highlight weekends in the plot. So that it could look something similar to this plot.
I am using Python with matplotlib and seaborn library.
You can easily highlight areas by using axvspan, to get the areas to be highlighted you can run through the index of your dataframe and search for the weekend days. I've also added an example for highlighting 'occupied hours' during a working week (hopefully that doesn't confuse things).
I've created dummy data for a dataframe based on days and another one for hours.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# dummy data (Days)
dates_d = pd.date_range('2017-01-01', '2017-02-01', freq='D')
df = pd.DataFrame(np.random.randint(1, 20, (dates_d.shape[0], 1)))
df.index = dates_d
# dummy data (Hours)
dates_h = pd.date_range('2017-01-01', '2017-02-01', freq='H')
df_h = pd.DataFrame(np.random.randint(1, 20, (dates_h.shape[0], 1)))
df_h.index = dates_h
#two graphs
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)
#plot lines
dfs = [df, df_h]
for i, df in enumerate(dfs):
for v in df.columns.tolist():
axes[i].plot(df[v], label=v, color='black', alpha=.5)
def find_weekend_indices(datetime_array):
indices = []
for i in range(len(datetime_array)):
if datetime_array[i].weekday() >= 5:
indices.append(i)
return indices
def find_occupied_hours(datetime_array):
indices = []
for i in range(len(datetime_array)):
if datetime_array[i].weekday() < 5:
if datetime_array[i].hour >= 7 and datetime_array[i].hour <= 19:
indices.append(i)
return indices
def highlight_datetimes(indices, ax):
i = 0
while i < len(indices)-1:
ax.axvspan(df.index[indices[i]], df.index[indices[i] + 1], facecolor='green', edgecolor='none', alpha=.5)
i += 1
#find to be highlighted areas, see functions
weekend_indices = find_weekend_indices(df.index)
occupied_indices = find_occupied_hours(df_h.index)
#highlight areas
highlight_datetimes(weekend_indices, axes[0])
highlight_datetimes(occupied_indices, axes[1])
#formatting..
axes[0].xaxis.grid(b=True, which='major', color='black', linestyle='--', alpha=1) #add xaxis gridlines
axes[1].xaxis.grid(b=True, which='major', color='black', linestyle='--', alpha=1) #add xaxis gridlines
axes[0].set_xlim(min(dates_d), max(dates_d))
axes[0].set_title('Weekend days', fontsize=10)
axes[1].set_title('Occupied hours', fontsize=10)
plt.show()
I tried using the code in the accepted answer but the way the indices are used, the last weekend in the time series does not get highlighted entirely, despite what the image currently shown suggests (this is noticeable mainly with a frequency of 6 hours or more). Also, it does not work if the frequency of the data is higher than daily. This is why I share here a solution that uses the x-axis units so that weekends (or any other recurring time period) can be highlighted without any problem related to the index.
This solution takes only 6 lines of code and it works with any frequency. In the example below, it highlights full weekend days which makes it more efficient than the accepted answer where small frequencies (e.g. 30 minutes) will produce many polygons to cover the whole weekend.
The x-axis limits are used to compute the range of time covered by the plot in terms of days, which is the unit used for matplotlib dates. Then a weekends mask is computed and passed to the where argument of the fill_between plotting function. The masks are processed as right-exclusive so in this case, they must contain Mondays for the highlights to be drawn up to Mondays 00:00. Because plotting these highlights can alter the x-axis limits when weekends occur near the limits, the x-axis limits are set back to the original values after plotting.
Note that contrary to axvspan, the fill_between function needs the y1 and y2 arguments. For some reason, using the default y-axis limits leaves a small gap between the plot frame and the tops and bottoms of the weekend highlights. This issue is solved by running ax.set_ylim(*ax.get_ylim()) just after creating the plot.
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
import matplotlib.dates as mdates
# Create sample dataset
rng = np.random.default_rng(seed=1234) # random number generator
dti = pd.date_range('2017-01-01', '2017-05-15', freq='D')
counts = 5000 + np.cumsum(rng.integers(-1000, 1000, size=dti.size))
df = pd.DataFrame(dict(Counts=counts), index=dti)
# Draw pandas plot: x_compat=True converts the pandas x-axis units to matplotlib
# date units (not strictly necessary when using a daily frequency like here)
ax = df.plot(x_compat=True, figsize=(10, 5), legend=None, ylabel='Counts')
ax.set_ylim(*ax.get_ylim()) # reset y limits to display highlights without gaps
# Highlight weekends based on the x-axis units
xmin, xmax = ax.get_xlim()
days = np.arange(np.floor(xmin), np.ceil(xmax)+2)
weekends = [(dt.weekday()>=5)|(dt.weekday()==0) for dt in mdates.num2date(days)]
ax.fill_between(days, *ax.get_ylim(), where=weekends, facecolor='k', alpha=.1)
ax.set_xlim(xmin, xmax) # set limits back to default values
# Create appropriate ticks using matplotlib date tick locators and formatters
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.MonthLocator(bymonthday=np.arange(5, 31, step=7)))
ax.xaxis.set_major_formatter(mdates.DateFormatter('\n%b'))
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%d'))
# Additional formatting
ax.figure.autofmt_xdate(rotation=0, ha='center')
title = 'Daily count of trips with weekends highlighted from SAT 00:00 to MON 00:00'
ax.set_title(title, pad=20, fontsize=14);
As you can see, the weekends are always highlighted to the full extent, regardless of where the data starts and ends.
You can find more examples of this solution in the answers I have posted here and here.
I have another suggestion to make in this regard, which takes inspirations from previous posts by other contributors. The code is as follows:
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
rng = np.random.default_rng(seed=42) # random number generator
dti = pd.date_range('2021-08-01', '2021-08-31', freq='D')
counts = 5000 + np.cumsum(rng.integers(-1000, 1000, size=dti.size))
df = pd.DataFrame(dict(Counts=counts), index=dti)
weekends = [d for d in df.index if d.isoweekday() in [6,7]]
weekend_list = []
for weekendday in weekends:
d1 = weekendday
d2 = weekendday + datetime.timedelta(days=1)
weekend_list.append((d1, d2))
weekend_df = pd.DataFrame(weekend_list)
sns.set()
plt.figure(figsize=(15, 10), dpi=100)
df.plot()
plt.legend(bbox_to_anchor=(1.02, 0), loc="lower left", borderaxespad=0)
plt.ylabel("Counts")
plt.xlabel("Date of visit")
plt.xticks(rotation = 0)
plt.title("Daily counts of shop visits with weekends highlighted in green")
ax = plt.gca()
for d in weekend_df.index:
print(weekend_df[0][d], weekend_df[1][d])
ax.axvspan(weekend_df[0][d], weekend_df[1][d], facecolor="g", edgecolor="none", alpha=0.5)
ax.relim()
ax.autoscale_view()
plt.savefig("junk.png", dpi=100, bbox_inches='tight', pad_inches=0.2)
The result would be something like the following diagram:
I am unable to show a bar and line graph on the same plot. Example code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Df = pd.DataFrame(data=np.random.randn(10,4), index=pd.DatetimeIndex(start='2005', freq='M', periods=10), columns=['A','B','C','D'])
fig = plt.figure()
ax = fig.add_subplot(111)
Df[['A','B']].plot(kind='bar', ax=ax)
Df[['C','D']].plot(ax=ax, color=['r', 'c'])
You can also try this:
fig = plt.figure()
ax = DF['A','B'].plot(kind="bar");plt.xticks(rotation=0)
ax2 = ax.twinx()
ax2.plot(ax.get_xticks(),DF['C','D'],marker='o')
I wanted to know as well, however all existing answers are not for showing bar and line graph on the same plot, but on different axis instead.
so I looked for the answer myself and have found an example that is working -- Plot Pandas DataFrame as Bar and Line on the same one chart. I can confirm that it works.
What baffled me was that, the almost same code works there but does not work here. I.e., I copied the OP's code and can verify that it is not working as expected.
The only thing I could think of is to add the index column to Df[['A','B']] and Df[['C','D']], but I don't know how since the index column doesn't have a name for me to add.
Today, I realize that even I can make it works, the real problem is that Df[['A','B']] gives a grouped (clustered) bar chart, but grouped (clustered) line chart is not supported.
The issue is that the pandas bar plot function treats the dates as a categorical variable where each date is considered to be a unique category, so the x-axis units are set to integers starting at 0 (like the default DataFrame index when none is assigned).
The pandas line plot uses x-axis units corresponding to the DatetimeIndex, for which 0 is located on January 1970 and the integers count the number of periods (months in this example) since then. So let's take a look at what happens in this particular case:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
# Create random data
rng = np.random.default_rng(seed=1) # random number generator
df = pd.DataFrame(data=rng.normal(size=(10,4)),
index=pd.date_range(start='2005', freq='M', periods=10),
columns=['A','B','C','D'])
# Create a pandas bar chart overlaid with a pandas line plot using the same
# Axes: note that seeing as I do not set any variable for x, df.index is used
# by default, which is usually what we want when dealing with a dataset
# containing a time series
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax);
The bars are nowhere to be seen. If you check what x ticks are being used, you'll see that the single major tick placed on January is 420 followed by these minor ticks for the other months:
ax.get_xticks(minor=True)
# [421, 422, 423, 424, 425, 426, 427, 428, 429]
This is because there are 35 years * 12 months since 1970, the numbering starts at 0 so January 2005 lands on 420. This explains why we do not see the bars. If you change the x-axis limit to start from zero, here is what you get:
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax)
ax.set_xlim(0);
The bars are squashed to the left, starting on January 1970. This problem can be solved by setting use_index=False in the line plot function so that the lines also start at 0:
ax = df.plot.bar(y=['A','B'], figsize=(9,5))
df.plot(y=['C','D'], color=['tab:green', 'tab:red'], ax=ax, use_index=False)
ax.set_xticklabels(df.index.strftime('%b'), rotation=0, ha='center');
# # Optional: move legend to new position
# import matplotlib.pyplot as plt # v 3.3.2
# ax.legend().remove()
# plt.gcf().legend(loc=(0.08, 0.14));
In case you want more advanced tick label formatting, you can check out the answers to this question which are compatible with this example. If you need more flexible/automated tick label formatting as provided by the tick locators and formatters in the matplotlib.dates module, the easiest is to create the plot with matplotlib like in this answer.
You can do something like that, both on the same figure:
In [4]: Df = pd.DataFrame(data=np.random.randn(10,4), index=pd.DatetimeIndex(start='2005', freq='M', periods=10), columns=['A','B','C','D'])
In [5]: fig, ax = plt.subplots(2, 1) # you can pass sharex=True, sharey=True if you want to share axes.
In [6]: Df[['A','B']].plot(kind='bar', ax=ax[0])
Out[6]: <matplotlib.axes.AxesSubplot at 0x10cf011d0>
In [7]: Df[['C','D']].plot(color=['r', 'c'], ax=ax[1])
Out[7]: <matplotlib.axes.AxesSubplot at 0x10a656ed0>