Develop Reference

How to separate 2 small pecentage values in matplotlib pie chart [duplicate]

My Python code is:
values = [234, 64, 54,10, 0, 1, 0, 9, 2, 1, 7, 7]
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul','Aug','Sep','Oct', 'Nov','Dec']
colors = ['yellowgreen', 'red', 'gold', 'lightskyblue',
'white','lightcoral','blue','pink', 'darkgreen',
'yellow','grey','violet','magenta','cyan']
plt.pie(values, labels=labels, autopct='%1.1f%%', shadow=True,
colors=colors, startangle=90, radius=1.2)
plt.show()
Is it possible to show the labels "Jan", "Feb", "Mar", etc. and the percentages, either:
without overlapping, or
using an arrow mark?

Alternatively you can put the legends beside the pie graph:
import matplotlib.pyplot as plt
import numpy as np
x = np.char.array(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct', 'Nov','Dec'])
y = np.array([234, 64, 54,10, 0, 1, 0, 9, 2, 1, 7, 7])
colors = ['yellowgreen','red','gold','lightskyblue','white','lightcoral','blue','pink', 'darkgreen','yellow','grey','violet','magenta','cyan']
porcent = 100.*y/y.sum()
patches, texts = plt.pie(y, colors=colors, startangle=90, radius=1.2)
labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(x, porcent)]
sort_legend = True
if sort_legend:
patches, labels, dummy = zip(*sorted(zip(patches, labels, y),
key=lambda x: x[2],
reverse=True))
plt.legend(patches, labels, loc='left center', bbox_to_anchor=(-0.1, 1.),
fontsize=8)
plt.savefig('piechart.png', bbox_inches='tight')
EDIT: if you want to keep the legend in the original order, as you mentioned in the comments, you can set sort_legend=False in the code above, giving:

If anyone just wants to offset the labels automatically, and not use a legend, I wrote this function that does it (yup I'm a real try-hard). It uses numpy but could easily be re-written in pure python.
import numpy as np
def fix_labels(mylabels, tooclose=0.1, sepfactor=2):
vecs = np.zeros((len(mylabels), len(mylabels), 2))
dists = np.zeros((len(mylabels), len(mylabels)))
for i in range(0, len(mylabels)-1):
for j in range(i+1, len(mylabels)):
a = np.array(mylabels[i].get_position())
b = np.array(mylabels[j].get_position())
dists[i,j] = np.linalg.norm(a-b)
vecs[i,j,:] = a-b
if dists[i,j] < tooclose:
mylabels[i].set_x(a[0] + sepfactor*vecs[i,j,0])
mylabels[i].set_y(a[1] + sepfactor*vecs[i,j,1])
mylabels[j].set_x(b[0] - sepfactor*vecs[i,j,0])
mylabels[j].set_y(b[1] - sepfactor*vecs[i,j,1])
So use it like:
wedges, labels, autopct = ax1.pie(sizes, labels=groups, autopct='%1.1f%%',
shadow=False, startangle=90)
fix_labels(autopct, sepfactor=3)
fix_labels(labels, sepfactor=2)
This works well as-written if you only have a few labels overlapping. If you have a whole bunch like OP, you might want to add a random direction vector to the vecs[i,j,:] = a-b line. That would probably work well.

Try tightlayout.
plt.tight_layout()
at the end of your code. It may prevent the overlap a little bit.

First of all; avoid pie charts whenever you can!
Secondly, have a think about how objects work in python. I believe this example should be self-explaining, however, you obviously don't need to move labels manually.
from matplotlib import pyplot as plt
fig, ax = plt.subplots()
ax.axis('equal')
patches, texts, autotexts = ax.pie([12,6,2,3],
labels=['A', 'B', 'C', 'no data'],
autopct='%1.1f%%',
pctdistance=0.5,
labeldistance=1.1)
# Move a label
texts[1]._x =-0.5
texts[1]._y =+0.5
# E.g. change some formatting
texts[-1]._color = 'blue'
There are some options to modify the labels:
# Check all options
print(texts[0].__dict__)
returns
{'_stale': False,
'stale_callback': <function matplotlib.artist._stale_axes_callback(self, val)>,
'_axes': <AxesSubplot:>,
'figure': <Figure size 432x288 with 1 Axes>,
'_transform': <matplotlib.transforms.CompositeGenericTransform at 0x7fe09bedf210>,
'_transformSet': True,
'_visible': True,
'_animated': False,
'_alpha': None,
'clipbox': <matplotlib.transforms.TransformedBbox at 0x7fe065d3dd50>,
'_clippath': None,
'_clipon': False,
'_label': '',
'_picker': None,
'_contains': None,
'_rasterized': None,
'_agg_filter': None,
'_mouseover': False,
'eventson': False,
'_oid': 0,
'_propobservers': {},
'_remove_method': <function list.remove(value, /)>,
'_url': None,
'_gid': None,
'_snap': None,
'_sketch': None,
'_path_effects': [],
'_sticky_edges': _XYPair(x=[], y=[]),
'_in_layout': True,
'_x': -0.07506663683168735,
'_y': 1.097435647331897,
'_text': 'A',
'_color': 'black',
'_fontproperties': <matplotlib.font_manager.FontProperties at 0x7fe065d3db90>,
'_usetex': False,
'_wrap': False,
'_verticalalignment': 'center',
'_horizontalalignment': 'right',
'_multialignment': None,
'_rotation': 'horizontal',
'_bbox_patch': None,
'_renderer': <matplotlib.backends.backend_agg.RendererAgg at 0x7fe08b01fd90>,
'_linespacing': 1.2,
'_rotation_mode': None}

how to add image to plot mplfinance python

Trying to add image and price label and add more space on time and it seems like ylim= takes care of that but when i add it my whole graph disappears.
market_colors = mpf.make_marketcolors(
base_mpf_style="charles"
)
rc = {
"axes.labelcolor": "none",
"axes.spines.bottom": True,
"axes.spines.left": False,
"axes.spines.right": False,
"axes.spines.top": False,
"font.size": 10,
}
styles = mpf.make_mpf_style(
base_mpf_style="nightclouds",
marketcolors=market_colors,
gridstyle="",
rc=rc
)
filledShape = {
"y1": df['Close'].values,
"facecolor": "#2279e4"
}
(mpf.plot(df, type='line',
title='Test',
linecolor='white',
style=styles,
volume=True,
figsize=(8, 6),
figscale=0.5,
fill_between=filledShape, tight_layout=True,
scale_padding={'left': 1, 'top': 5, 'right': 1, 'bottom': 2}
))

There are three techniques that I know of to display an image on a matplotlib plot:
Axes.imshow()
Figure.figimage()
Putting the image in an AnnotationBbox
In terms of working with mplfinance, I would say that technique one, calling Axes.imshow() is probably simplest:
Step 1:
For all three of the above techniques, when you call mpf.plot() set kwarg returnfig=True:
fig axlist = mpf.plot(df,...,returnfig=True)
This will give you access to the mplfinance Figure and Axes objects.
Step 2:
Now create a new Axes object where you want the image/logo:
# Note: [left,bottom,width,height] are in terms of fraction of the Figure.
# For example [0.05,0.08,0.10,0.06] means:
# the lower/left corner of the Axes will be located:
# 5% of the way in from the left
# 8% down from the top,
# and the Axes will be
# 10% of the Figure wide and
# 6% of the Figure high.
logo_axes = fig.add_axes([left,bottom,width,height])
Step 3:
Read in the image:
import Image
im = Image.open('image_file_name.png')
Step 4:
Call imshow() on the newly created Axes, and turn of the axis lines:
logo_axes.imshow(im)
logo_axes.axis('off')
Step 5:
Since returnfig=True causes mplfinance to not show the Figure, call mpf.show()
mpf.show()

I'm not sure if this answer will help you or not since I'm not sure what kind of images you want to add. I assume you want to add a corporate logo or something like that, so I did some research and found an answer to whether you can add a watermark to an mpf. I used this answer as a guide and added the icons used on stackoveflow.com to the graph. However, it was not possible to add them to the axes, so I had to add them to the fig. I have changed the style to add the image.
img = plt.imread('./data/se-icon.png')
market_colors = mpf.make_marketcolors(
base_mpf_style="charles"
)
rc = {
"axes.labelcolor": "none",
"axes.spines.bottom": True,
"axes.spines.left": False,
"axes.spines.right": False,
"axes.spines.top": False,
"font.size": 10,
}
styles = mpf.make_mpf_style(
base_mpf_style="yahoo",# nightclouds
marketcolors=market_colors,
gridstyle="",
rc=rc
)
filledShape = {
"y1": df['Close'].values,
"facecolor": "#2279e4"
}
fig, axes = mpf.plot(df, type='line',
title='Test',
linecolor='white',
style=styles,
volume=True,
figsize=(8, 6),
figscale=0.5,
fill_between=filledShape,
tight_layout=True,
scale_padding={'left': 1, 'top': 5, 'right': 1, 'bottom': 2},
returnfig=True
)
#axes[0].imshow(img)
#height = img.shape[1]
fig.figimage(img, 0, fig.bbox.ymax - height*1.5)
plt.show()

Plot outliers using matplotlib and seaborn

I have performed outlier detection on some entrance sensor data for a shopping mall. I want create one plot for each entrance and highlight the observations that are outliers (which are marked by True in the outlier column in the dataframe).
Here is a small snippet of the data for two entrances and a time span of six days:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({"date": [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6],
"mall": ["Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1", "Mall1"],
"entrance": ["West", "West","West","West","West", "West", "East", "East", "East", "East", "East", "East"],
"in": [132, 140, 163, 142, 133, 150, 240, 250, 233, 234, 2000, 222],
"outlier": [False, False, False, False, False, False, False, False, False, False, True, False]})
In order to create several plots (there are twenty entrances in the full data), I have come across lmplot in seaborn.
sns.set_theme(style="darkgrid")
for i, group in df.groupby('entrance'):
sns.lmplot(x="date", y="in", data=group, fit_reg=False, hue = "entrance")
#pseudo code
#for the rows that have an outlier (outlier = True) create a red dot for that observation
plt.show()
There are two things I would like to accomplish here:
Lineplot instead of scatterplot. I have not been successful in using sns.lineplot for creating separate plots for each entrance, as it seems lmplot is more fit for this.
For each entrance plot, I would like show which of the observations that are outliers, preferably as a red dot. I have tried writing some pseudo code in my plotting attempts.

seaborn.lmplot is a Facetgrid, which I think is more difficult to use, in this case.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
for i, group in df.groupby(['entrance']):
# plot all the values as a lineplot
sns.lineplot(x="date", y="in", data=group)
# select the data when outlier is True and plot it
data_t = group[group.outlier == True]
sns.scatterplot(x="date", y="in", data=data_t, c=['r'])
# add a title using the value from the groupby
plt.title(f'Entrance: {i}')
# show the plot here, not outside the loop
plt.show()
Alternate option
This option will allow for setting the number of columns and rows of a figure
import math
# specify the number of columns to plot
ncols = 2
# determine the number of rows, even if there's an odd number of unique entrances
nrows = math.ceil(len(df.entrance.unique()) / ncols)
fig, axes = plt.subplots(ncols=ncols, nrows=nrows, figsize=(16, 16))
# extract the axes into an nx1 array, which is easier to index with idx.
axes = axes.ravel()
for idx, (i, group) in enumerate(df.groupby(['entrance'])):
# plot all the values as a lineplot
sns.lineplot(x="date", y="in", data=group, ax=axes[idx])
# select the data when outlier is True and plot it
data_t = group[group.outlier == True]
sns.scatterplot(x="date", y="in", data=data_t, c=['r'], ax=axes[idx])
axes[idx].set_title(f'Entrance: {i}')

How to enable and disable the logarithmic scale as a viewer in Plotly?

I am recently exploring Plotly and I wonder if there is a way for sharing a plot and let the viewer switch between a logarithmic axis and linear axis.
Any suggestion?

Plotly has a dropdown feature which allows the user to dynamically update the plot styling and/or the traces being displayed. Below is a minimal working example of a plot where the user can switch between a logarithmic and linear scale.
import plotly
import plotly.graph_objs as go
x = [1, 2, 3]
y = [1000, 10000, 100000]
y2 = [5000, 10000, 90000]
trace1 = go.Bar(x=x, y=y, name='trace1')
trace2 = go.Bar(x=x, y=y2, name='trace2', visible=False)
data = [trace1, trace2]
updatemenus = list([
dict(active=1,
buttons=list([
dict(label='Log Scale',
method='update',
args=[{'visible': [True, True]},
{'title': 'Log scale',
'yaxis': {'type': 'log'}}]),
dict(label='Linear Scale',
method='update',
args=[{'visible': [True, False]},
{'title': 'Linear scale',
'yaxis': {'type': 'linear'}}])
]),
)
])
layout = dict(updatemenus=updatemenus, title='Linear scale')
fig = go.Figure(data=data, layout=layout)
plotly.offline.iplot(fig)
I added two traces to the data list to show how traces can also be added or removed from a plot. This can be controlled by the visible list in updatemenus for each button.

scatterplot in python, make color and shape according to different categorical variables

I need to generate a scatter plot, where multiple categorical variables can be represented by color and shape of the scatters. For example,
df = pd.DataFrame({'animals':pd.Series(['tiger','cheetah','lion','giraffe','elephant','gorilla']),
'weight': pd.Series([200,120,240,240,400,300]),
'meal': pd.Series([20,10,40,15,40,30]),
'region': pd.Categorical(["Asian","American","African","African",'Asian','American']),
'gender': pd.Categorical(["female","female","male","female",'male','male']),
'group': pd.Series([True, False, False, True, True, True])})
sns.lmplot(data = df, x = 'weight', y = 'meal', hue = 'region', palette = 'Dark2',
fit_reg = False, scatter_kws={"s": 20}, size = 4)
This will create a plot, where the region is represented by different colors.
But I also need to represent the gender, in different shapes. So the color shows the region and the shape shows the gender.
Is there a way to achieve this? thanks for any suggestion!
enter image description here

You need to simply pass a list of markers in your call to sns.lmplot:
import seaborn as sns
df = pd.DataFrame({'animals':pd.Series(['tiger','cheetah','lion','giraffe','elephant','gorilla']),
'weight': pd.Series([200,120,240,240,400,300]),
'meal': pd.Series([20,10,40,15,40,30]),
'region': pd.Categorical(["Asian","American","African","African",'Asian','American']),
'gender': pd.Categorical(["female","female","male","female",'male','male']),
'group': pd.Series([True, False, False, True, True, True])})
sns.lmplot(data = df, x = 'weight', y = 'meal', hue = 'region', palette = 'Dark2',
fit_reg = False, scatter_kws={"s": 20}, size = 4, markers=["o","+","x"])
plt.show()
Which gives: