Related
I'm running into issues with some subplots. I've provided some sample code to generate the types of plots I would like to create. I'd like these to be the same size, side by side.
I'm am having a really hard time figuring out how to create the subplots though. I keep running into some issues with the thetagrids here. This is what i've tried. I can get these to work seprarately, but cant figure out how to combine them. Eventually I might want a third plot as well.
import numpy as np
import matplotlib.pyplot as plt
## Plot 1
x1 = np.array([0, 1, 2, 3])
y1 = np.array([7, 2, 4, 2])
plt.subplot(1, 2, 1)
plt.figure(figsize=(5, 5))
plt.scatter(x1, y1)
# plt.show()
### Plot 2
# make up data for plot
polar_list = ['a', 'b', 'c', 'd', 'a']
polar_points = [4, 3, 6, 7, 4]
# modify lists for plots
label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(polar_list))
plt.figure(figsize=(5, 5))
plt.subplot(1, 2, 2, polar=True)
plt.plot(label_loc, polar_points, label='DataLable')
plt.title('DataLable comparison', size=20, y=1.05)
lines, labels = plt.thetagrids(np.degrees(label_loc), labels=polar_list)
plt.legend()
plt.show()
You are creating a new figure every time you call plt.figure(). Just place one at the very beginning and then the plt.subplot() will add subplots to the figures.
import numpy as np
import matplotlib.pyplot as plt
## Plot 1
x1 = np.array([0, 1, 2, 3])
y1 = np.array([7, 2, 4, 2])
plt.figure(figsize= (5, 5))
plt.subplot(1, 2, 1)
plt.scatter(x1, y1)
# plt.show()
### Plot 2
# make up data for plot
polar_list = ['a', 'b', 'c', 'd', 'a']
polar_points = [4, 3, 6, 7, 4]
# modify lists for plots
label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(polar_list))
plt.subplot(1, 2, 2, polar=True)
plt.plot(label_loc, polar_points, label='DataLable')
plt.title('DataLable comparison', size=20, y=1.05)
lines, labels = plt.thetagrids(np.degrees(label_loc), labels=polar_list)
plt.legend()
plt.show()
I have a dataframe df which looks as follows:
A B
X 5 7
Y 10 5
df.to_dict() gives the following:
{'A': {'X': 5, 'Y': 10}, 'B': {'X': 7, 'Y': 5}}
I have created a stacked bar plot using
df.plot(kind = "bar", stacked = True)
It look as follows:
I want to shade the region between A in X and Y bar, and the same for B. The shaded region reflect how the value of A and B has changed between X and Y. It should look something as shown:
How can I fill areas in between these two stacked bar plots using matplotlib in Python keeping the original structure of bar plot intact?
Here's another fill_between with more general approach:
# loop through the bars to get the bottom and top points
bottoms = []
tops = []
for patch in ax.patches:
x,y = patch.get_xy()
w,h = patch.get_width(), patch.get_height()
bottoms += [(x,y), (x+w, y)]
tops += [(x, y+h), (x+w, y+h)]
# convert to numpy for easy slicing
tops = np.array(tops)
bottoms = np.array(bottoms)
# extract the x coordinates
x = np.unique(bottoms[:,0])
num_x = len(x)
# fill between each bottom and top pairs
for i in range(0, len(bottoms), num_x):
plt.fill_between(x, tops[i:i+num_x, 1], bottoms[i:i+num_x, 1], alpha=0.5)
Output:
Here is a way using fill_between.
ax = df.plot(kind = "bar", stacked = True)
plt.fill_between(x = [ax.patches[0].get_x() + ax.patches[0].get_width(),
ax.patches[1].get_x()],
y1 = 0,
y2 = [ax.patches[0].get_y() + ax.patches[0].get_height(),
ax.patches[1].get_y() + ax.patches[1].get_height()],
color = ax.patches[0].get_facecolor(), alpha=0.5)
plt.fill_between(x = [ax.patches[2].get_x() + ax.patches[2].get_width(),
ax.patches[3].get_x()],
y1 = [ax.patches[0].get_y() + ax.patches[0].get_height(),
ax.patches[1].get_y() + ax.patches[1].get_height()],
y2 = [ax.patches[2].get_y() + ax.patches[2].get_height(),
ax.patches[3].get_y() + ax.patches[3].get_height()],
color = ax.patches[2].get_facecolor(), alpha=0.5)
plt.plot()
Ok. I found an easy way myself using ax.fill_between(). For x, I specify [0.25, 0.75]. 0.25 refers to the right edge of bar for X and 0.75 refers to the left edge of bar for Y. The positions in X-axis with ticks for X and Y are 0 and 1 respectively.
For y1 and y2, I specify the y-coordinates of lower and upper edges to be filled in between respectively.
fig, ax = plt.subplots()
ax = df.plot(kind = "bar", stacked = True, ax = ax, color = ["blue","orange"])
ax.fill_between(x =[0.25,0.75], y1 = [0, 0], y2 = [5, 10], color = "blue", alpha = 0.5)
ax.fill_between(x =[0.25, 0.75], y1 = [5, 10], y2 = [12, 15], color = "orange", alpha = 0.5)
plt.show()
I get something as shown:
I am trying to plot a cluster using scatter plot with two x-axes, in left and right side, not in top and bottom. I have checked out similar questions but it doesn't seem to be the problem here.
I was trying to make this two plots share y axis with 2 x-axes in left and right side, but I cannot make it.
This is my plot,
but I expect my plot to be displayed like this
.
This is the code I have tried.
import pylab as py
filename1="ex_1.csv"
df1=pd.read_csv(filename1)
filename2="ex_21.csv"
df2=pd.read_csv(filename2)
x1 = df1['Dom']
y1 = df1['Sal']
s1 = df1['Size']
x2 = df2['Type']
y2 = df2['Sal']
s2 = df2['Size']
d = 2.0
#plot 1
fig1 = plt.figure(figsize=(10,6))
fig, (gb1, gb2) = plt.subplots(nrows = 1, ncols = 2, figsize=(10,6),sharey = True)
gb1.scatter(x=x1, y=y1, s=s1*50, alpha=0.8, c="blue", label=x1)
gb2.scatter(x=x2, y=y2, s=s2*50, alpha=0.8, c="red")
If your categories are different, you can just use a single plot and draw a line in between.
import matplotlib.pyplot as plt
import numpy as np
ax = plt.subplot(111)
x1 = np.array(['A','B','C','A','B','C'])
y1 = np.array([1,2,3,4,5,6])
x2 = np.array(['X', 'Y', 'X', 'Y', 'Z', 'Z'])
y2 = np.array([1, 2, 3, 4, 5, 6])
ax.scatter(x1, y1, s=y1*50, c='b')
ax.scatter(x2, y2, s=y2*50, c='r')
ax.axline((2.5, 1), (2.5, 6), c='black')
ax.figure.show()
Edit: As pointed out in the comment, changed import pylab to import matplotlib.pyplot as plt as pylab can cause unexpected behavior and is strongly discouraged by matplotlib
How to plot multiple bars in matplotlib, when I tried to call the bar function multiple times, they overlap and as seen the below figure the highest value red can be seen only.
How can I plot the multiple bars with dates on the x-axes?
So far, I tried this:
import matplotlib.pyplot as plt
import datetime
x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]
ax = plt.subplot(111)
ax.bar(x, y, width=0.5, color='b', align='center')
ax.bar(x, z, width=0.5, color='g', align='center')
ax.bar(x, k, width=0.5, color='r', align='center')
ax.xaxis_date()
plt.show()
I got this:
The results should be something like, but with the dates are on the x-axes and bars are next to each other:
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import datetime
x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
x = date2num(x)
y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]
ax = plt.subplot(111)
ax.bar(x-0.2, y, width=0.2, color='b', align='center')
ax.bar(x, z, width=0.2, color='g', align='center')
ax.bar(x+0.2, k, width=0.2, color='r', align='center')
ax.xaxis_date()
plt.show()
I don't know what's the "y values are also overlapping" means, does the following code solve your problem?
ax = plt.subplot(111)
w = 0.3
ax.bar(x-w, y, width=w, color='b', align='center')
ax.bar(x, z, width=w, color='g', align='center')
ax.bar(x+w, k, width=w, color='r', align='center')
ax.xaxis_date()
ax.autoscale(tight=True)
plt.show()
The trouble with using dates as x-values, is that if you want a bar chart like in your second picture, they are going to be wrong. You should either use a stacked bar chart (colours on top of each other) or group by date (a "fake" date on the x-axis, basically just grouping the data points).
import numpy as np
import matplotlib.pyplot as plt
N = 3
ind = np.arange(N) # the x locations for the groups
width = 0.27 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
yvals = [4, 9, 2]
rects1 = ax.bar(ind, yvals, width, color='r')
zvals = [1,2,3]
rects2 = ax.bar(ind+width, zvals, width, color='g')
kvals = [11,12,13]
rects3 = ax.bar(ind+width*2, kvals, width, color='b')
ax.set_ylabel('Scores')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('2011-Jan-4', '2011-Jan-5', '2011-Jan-6') )
ax.legend( (rects1[0], rects2[0], rects3[0]), ('y', 'z', 'k') )
def autolabel(rects):
for rect in rects:
h = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
plt.show()
after looking for a similar solution and not finding anything flexible enough, I decided to write my own function for it. It allows you to have as many bars per group as you wish and specify both the width of a group as well as the individual widths of the bars within the groups.
Enjoy:
from matplotlib import pyplot as plt
def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):
"""Draws a bar plot with multiple bars per data point.
Parameters
----------
ax : matplotlib.pyplot.axis
The axis we want to draw our plot on.
data: dictionary
A dictionary containing the data we want to plot. Keys are the names of the
data, the items is a list of the values.
Example:
data = {
"x":[1,2,3],
"y":[1,2,3],
"z":[1,2,3],
}
colors : array-like, optional
A list of colors which are used for the bars. If None, the colors
will be the standard matplotlib color cyle. (default: None)
total_width : float, optional, default: 0.8
The width of a bar group. 0.8 means that 80% of the x-axis is covered
by bars and 20% will be spaces between the bars.
single_width: float, optional, default: 1
The relative width of a single bar within a group. 1 means the bars
will touch eachother within a group, values less than 1 will make
these bars thinner.
legend: bool, optional, default: True
If this is set to true, a legend will be added to the axis.
"""
# Check if colors where provided, otherwhise use the default color cycle
if colors is None:
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
# Number of bars per group
n_bars = len(data)
# The width of a single bar
bar_width = total_width / n_bars
# List containing handles for the drawn bars, used for the legend
bars = []
# Iterate over all data
for i, (name, values) in enumerate(data.items()):
# The offset in x direction of that bar
x_offset = (i - n_bars / 2) * bar_width + bar_width / 2
# Draw a bar for every value of that type
for x, y in enumerate(values):
bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])
# Add a handle to the last drawn bar, which we'll need for the legend
bars.append(bar[0])
# Draw legend if we need
if legend:
ax.legend(bars, data.keys())
if __name__ == "__main__":
# Usage example:
data = {
"a": [1, 2, 3, 2, 1],
"b": [2, 3, 4, 3, 1],
"c": [3, 2, 1, 4, 2],
"d": [5, 9, 2, 1, 8],
"e": [1, 3, 2, 2, 3],
"f": [4, 3, 1, 1, 4],
}
fig, ax = plt.subplots()
bar_plot(ax, data, total_width=.8, single_width=.9)
plt.show()
Output:
I know that this is about matplotlib, but using pandas and seaborn can save you a lot of time:
df = pd.DataFrame(zip(x*3, ["y"]*3+["z"]*3+["k"]*3, y+z+k), columns=["time", "kind", "data"])
plt.figure(figsize=(10, 6))
sns.barplot(x="time", hue="kind", y="data", data=df)
plt.show()
Given the existing answers, the easiest solution, given the data in the OP, is load the data into a dataframe and plot with pandas.DataFrame.plot.
Load the value lists into pandas with a dict, and specify x as the index. The index will automatically be set as the x-axis, and the columns will be plotted as the bars.
pandas.DataFrame.plot uses matplotlib as the default backend.
See How to add value labels on a bar chart for thorough details about using .bar_label.
Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3
import pandas as pd
# using the existing lists from the OP, create the dataframe
df = pd.DataFrame(data={'y': y, 'z': z, 'k': k}, index=x)
# since there's no time component and x was a datetime dtype, set the index to be just the date
df.index = df.index.date
# display(df)
y z k
2011-01-04 4 1 11
2011-01-05 9 2 12
2011-01-06 2 3 13
# plot bars or kind='barh' for horizontal bars; adjust figsize accordingly
ax = df.plot(kind='bar', rot=0, xlabel='Date', ylabel='Value', title='My Plot', figsize=(6, 4))
# add some labels
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
# add a little space at the top of the plot for the annotation
ax.margins(y=0.1)
# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')
Horizontal bars for when there are more columns
ax = df.plot(kind='barh', ylabel='Date', title='My Plot', figsize=(5, 4))
ax.set(xlabel='Value')
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
ax.margins(x=0.1)
# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')
I modified pascscha's solution extending the interface, hopefully this helps someone else! Key features:
Variable number of entries per bar group
Customizable colors
Handling of x ticks
Fully customizable bar labels on top of bars
def bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95,
legend=True, x_labels=True, label_fontsize=8,
colors=None, barlabel_offset=1,
bar_labeler=lambda k, i, s: str(round(s, 3))):
"""
Draws a bar plot with multiple bars per data point.
:param dict data: The data we want to plot, wher keys are the names of each
bar group, and items is a list of bar values for the corresponding group.
:param float group_stretch: 1 means groups occupy the most (largest groups
touch side to side if they have equal number of bars).
:param float bar_stretch: If 1, bars within a group will touch side to side.
:param bool x_labels: If true, x-axis will contain labels with the group
names given at data, centered at the bar group.
:param int label_fontsize: Font size for the label on top of each bar.
:param float barlabel_offset: Distance, in y-values, between the top of the
bar and its label.
:param function bar_labeler: If not None, must be a functor with signature
``f(group_name, i, scalar)->str``, where each scalar is the entry found at
data[group_name][i]. When given, returns a label to put on the top of each
bar. Otherwise no labels on top of bars.
"""
sorted_data = list(sorted(data.items(), key=lambda elt: elt[0]))
sorted_k, sorted_v = zip(*sorted_data)
max_n_bars = max(len(v) for v in data.values())
group_centers = np.cumsum([max_n_bars
for _ in sorted_data]) - (max_n_bars / 2)
bar_offset = (1 - bar_stretch) / 2
bars = defaultdict(list)
#
if colors is None:
colors = {g_name: [f"C{i}" for _ in values]
for i, (g_name, values) in enumerate(data.items())}
#
for g_i, ((g_name, vals), g_center) in enumerate(zip(sorted_data,
group_centers)):
n_bars = len(vals)
group_beg = g_center - (n_bars / 2) + (bar_stretch / 2)
for val_i, val in enumerate(vals):
bar = ax.bar(group_beg + val_i + bar_offset,
height=val, width=bar_stretch,
color=colors[g_name][val_i])[0]
bars[g_name].append(bar)
if bar_labeler is not None:
x_pos = bar.get_x() + (bar.get_width() / 2.0)
y_pos = val + barlabel_offset
barlbl = bar_labeler(g_name, val_i, val)
ax.text(x_pos, y_pos, barlbl, ha="center", va="bottom",
fontsize=label_fontsize)
if legend:
ax.legend([bars[k][0] for k in sorted_k], sorted_k)
#
ax.set_xticks(group_centers)
if x_labels:
ax.set_xticklabels(sorted_k)
else:
ax.set_xticklabels()
return bars, group_centers
Sample run:
fig, ax = plt.subplots()
data = {"Foo": [1, 2, 3, 4], "Zap": [0.1, 0.2], "Quack": [6], "Bar": [1.1, 2.2, 3.3, 4.4, 5.5]}
bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95, legend=True,
labels=True, label_fontsize=8, barlabel_offset=0.05,
bar_labeler=lambda k, i, s: str(round(s, 3)))
fig.show()
I did this solution: if you want plot more than one plot in one figure, make sure before plotting next plots you have set right matplotlib.pyplot.hold(True)
to able adding another plots.
Concerning the datetime values on the X axis, a solution using the alignment of bars works for me. When you create another bar plot with matplotlib.pyplot.bar(), just use align='edge|center' and set width='+|-distance'.
When you set all bars (plots) right, you will see the bars fine.
How to plot multiple bars in matplotlib, when I tried to call the bar function multiple times, they overlap and as seen the below figure the highest value red can be seen only.
How can I plot the multiple bars with dates on the x-axes?
So far, I tried this:
import matplotlib.pyplot as plt
import datetime
x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]
ax = plt.subplot(111)
ax.bar(x, y, width=0.5, color='b', align='center')
ax.bar(x, z, width=0.5, color='g', align='center')
ax.bar(x, k, width=0.5, color='r', align='center')
ax.xaxis_date()
plt.show()
I got this:
The results should be something like, but with the dates are on the x-axes and bars are next to each other:
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import datetime
x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
x = date2num(x)
y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]
ax = plt.subplot(111)
ax.bar(x-0.2, y, width=0.2, color='b', align='center')
ax.bar(x, z, width=0.2, color='g', align='center')
ax.bar(x+0.2, k, width=0.2, color='r', align='center')
ax.xaxis_date()
plt.show()
I don't know what's the "y values are also overlapping" means, does the following code solve your problem?
ax = plt.subplot(111)
w = 0.3
ax.bar(x-w, y, width=w, color='b', align='center')
ax.bar(x, z, width=w, color='g', align='center')
ax.bar(x+w, k, width=w, color='r', align='center')
ax.xaxis_date()
ax.autoscale(tight=True)
plt.show()
The trouble with using dates as x-values, is that if you want a bar chart like in your second picture, they are going to be wrong. You should either use a stacked bar chart (colours on top of each other) or group by date (a "fake" date on the x-axis, basically just grouping the data points).
import numpy as np
import matplotlib.pyplot as plt
N = 3
ind = np.arange(N) # the x locations for the groups
width = 0.27 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
yvals = [4, 9, 2]
rects1 = ax.bar(ind, yvals, width, color='r')
zvals = [1,2,3]
rects2 = ax.bar(ind+width, zvals, width, color='g')
kvals = [11,12,13]
rects3 = ax.bar(ind+width*2, kvals, width, color='b')
ax.set_ylabel('Scores')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('2011-Jan-4', '2011-Jan-5', '2011-Jan-6') )
ax.legend( (rects1[0], rects2[0], rects3[0]), ('y', 'z', 'k') )
def autolabel(rects):
for rect in rects:
h = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),
ha='center', va='bottom')
autolabel(rects1)
autolabel(rects2)
autolabel(rects3)
plt.show()
after looking for a similar solution and not finding anything flexible enough, I decided to write my own function for it. It allows you to have as many bars per group as you wish and specify both the width of a group as well as the individual widths of the bars within the groups.
Enjoy:
from matplotlib import pyplot as plt
def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):
"""Draws a bar plot with multiple bars per data point.
Parameters
----------
ax : matplotlib.pyplot.axis
The axis we want to draw our plot on.
data: dictionary
A dictionary containing the data we want to plot. Keys are the names of the
data, the items is a list of the values.
Example:
data = {
"x":[1,2,3],
"y":[1,2,3],
"z":[1,2,3],
}
colors : array-like, optional
A list of colors which are used for the bars. If None, the colors
will be the standard matplotlib color cyle. (default: None)
total_width : float, optional, default: 0.8
The width of a bar group. 0.8 means that 80% of the x-axis is covered
by bars and 20% will be spaces between the bars.
single_width: float, optional, default: 1
The relative width of a single bar within a group. 1 means the bars
will touch eachother within a group, values less than 1 will make
these bars thinner.
legend: bool, optional, default: True
If this is set to true, a legend will be added to the axis.
"""
# Check if colors where provided, otherwhise use the default color cycle
if colors is None:
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
# Number of bars per group
n_bars = len(data)
# The width of a single bar
bar_width = total_width / n_bars
# List containing handles for the drawn bars, used for the legend
bars = []
# Iterate over all data
for i, (name, values) in enumerate(data.items()):
# The offset in x direction of that bar
x_offset = (i - n_bars / 2) * bar_width + bar_width / 2
# Draw a bar for every value of that type
for x, y in enumerate(values):
bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])
# Add a handle to the last drawn bar, which we'll need for the legend
bars.append(bar[0])
# Draw legend if we need
if legend:
ax.legend(bars, data.keys())
if __name__ == "__main__":
# Usage example:
data = {
"a": [1, 2, 3, 2, 1],
"b": [2, 3, 4, 3, 1],
"c": [3, 2, 1, 4, 2],
"d": [5, 9, 2, 1, 8],
"e": [1, 3, 2, 2, 3],
"f": [4, 3, 1, 1, 4],
}
fig, ax = plt.subplots()
bar_plot(ax, data, total_width=.8, single_width=.9)
plt.show()
Output:
I know that this is about matplotlib, but using pandas and seaborn can save you a lot of time:
df = pd.DataFrame(zip(x*3, ["y"]*3+["z"]*3+["k"]*3, y+z+k), columns=["time", "kind", "data"])
plt.figure(figsize=(10, 6))
sns.barplot(x="time", hue="kind", y="data", data=df)
plt.show()
Given the existing answers, the easiest solution, given the data in the OP, is load the data into a dataframe and plot with pandas.DataFrame.plot.
Load the value lists into pandas with a dict, and specify x as the index. The index will automatically be set as the x-axis, and the columns will be plotted as the bars.
pandas.DataFrame.plot uses matplotlib as the default backend.
See How to add value labels on a bar chart for thorough details about using .bar_label.
Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3
import pandas as pd
# using the existing lists from the OP, create the dataframe
df = pd.DataFrame(data={'y': y, 'z': z, 'k': k}, index=x)
# since there's no time component and x was a datetime dtype, set the index to be just the date
df.index = df.index.date
# display(df)
y z k
2011-01-04 4 1 11
2011-01-05 9 2 12
2011-01-06 2 3 13
# plot bars or kind='barh' for horizontal bars; adjust figsize accordingly
ax = df.plot(kind='bar', rot=0, xlabel='Date', ylabel='Value', title='My Plot', figsize=(6, 4))
# add some labels
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
# add a little space at the top of the plot for the annotation
ax.margins(y=0.1)
# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')
Horizontal bars for when there are more columns
ax = df.plot(kind='barh', ylabel='Date', title='My Plot', figsize=(5, 4))
ax.set(xlabel='Value')
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
ax.margins(x=0.1)
# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')
I modified pascscha's solution extending the interface, hopefully this helps someone else! Key features:
Variable number of entries per bar group
Customizable colors
Handling of x ticks
Fully customizable bar labels on top of bars
def bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95,
legend=True, x_labels=True, label_fontsize=8,
colors=None, barlabel_offset=1,
bar_labeler=lambda k, i, s: str(round(s, 3))):
"""
Draws a bar plot with multiple bars per data point.
:param dict data: The data we want to plot, wher keys are the names of each
bar group, and items is a list of bar values for the corresponding group.
:param float group_stretch: 1 means groups occupy the most (largest groups
touch side to side if they have equal number of bars).
:param float bar_stretch: If 1, bars within a group will touch side to side.
:param bool x_labels: If true, x-axis will contain labels with the group
names given at data, centered at the bar group.
:param int label_fontsize: Font size for the label on top of each bar.
:param float barlabel_offset: Distance, in y-values, between the top of the
bar and its label.
:param function bar_labeler: If not None, must be a functor with signature
``f(group_name, i, scalar)->str``, where each scalar is the entry found at
data[group_name][i]. When given, returns a label to put on the top of each
bar. Otherwise no labels on top of bars.
"""
sorted_data = list(sorted(data.items(), key=lambda elt: elt[0]))
sorted_k, sorted_v = zip(*sorted_data)
max_n_bars = max(len(v) for v in data.values())
group_centers = np.cumsum([max_n_bars
for _ in sorted_data]) - (max_n_bars / 2)
bar_offset = (1 - bar_stretch) / 2
bars = defaultdict(list)
#
if colors is None:
colors = {g_name: [f"C{i}" for _ in values]
for i, (g_name, values) in enumerate(data.items())}
#
for g_i, ((g_name, vals), g_center) in enumerate(zip(sorted_data,
group_centers)):
n_bars = len(vals)
group_beg = g_center - (n_bars / 2) + (bar_stretch / 2)
for val_i, val in enumerate(vals):
bar = ax.bar(group_beg + val_i + bar_offset,
height=val, width=bar_stretch,
color=colors[g_name][val_i])[0]
bars[g_name].append(bar)
if bar_labeler is not None:
x_pos = bar.get_x() + (bar.get_width() / 2.0)
y_pos = val + barlabel_offset
barlbl = bar_labeler(g_name, val_i, val)
ax.text(x_pos, y_pos, barlbl, ha="center", va="bottom",
fontsize=label_fontsize)
if legend:
ax.legend([bars[k][0] for k in sorted_k], sorted_k)
#
ax.set_xticks(group_centers)
if x_labels:
ax.set_xticklabels(sorted_k)
else:
ax.set_xticklabels()
return bars, group_centers
Sample run:
fig, ax = plt.subplots()
data = {"Foo": [1, 2, 3, 4], "Zap": [0.1, 0.2], "Quack": [6], "Bar": [1.1, 2.2, 3.3, 4.4, 5.5]}
bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95, legend=True,
labels=True, label_fontsize=8, barlabel_offset=0.05,
bar_labeler=lambda k, i, s: str(round(s, 3)))
fig.show()
I did this solution: if you want plot more than one plot in one figure, make sure before plotting next plots you have set right matplotlib.pyplot.hold(True)
to able adding another plots.
Concerning the datetime values on the X axis, a solution using the alignment of bars works for me. When you create another bar plot with matplotlib.pyplot.bar(), just use align='edge|center' and set width='+|-distance'.
When you set all bars (plots) right, you will see the bars fine.