I would like to write scout report on some football players and for that I need visualizations. One type of which is pie charts. Now I need some pie charts that looks like below, with different size of slices ( proportionate to the number of the thing the slice indicates) . Can anyone suggest how to do it or have any link to websites where I can learn this?
What you are looking for is called a "Radar Pie Chart". It's analogous to the more commonly used "Radar Chart", but I think it looks better as it highlights the values, rather than focus on meaningless shapes.
The challenge you face with your football dataset is that each category is on a different scale, so you want to plot each value as a percentage of some max. My code will accomplish that, but you'll want to annotate the original values to finish off these charts.
The plot itself can be done with just the standard matplotlib library using polar axes. I borrowed code from here (https://raphaelletseng.medium.com/getting-to-know-matplotlib-and-python-docx-5ee67bad38d2).
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from math import pi
from random import random, seed
seed(12345)
# Generate dataset with 10 rows, different maxes
maxes = [5, 5, 5, 2, 2, 10, 10, 10, 10, 10]
df = pd.DataFrame(
data = {
'categories': ['category_{}'.format(x) for x, _ in enumerate(maxes)],
'scores': [random()*max for max in maxes],
'max_values': maxes,
},
)
df['pct'] = df['scores'] / df['max_values']
df = df.set_index('categories')
# Plot pie radar chart
N = df.shape[0]
theta = np.linspace(0.0, 2*np.pi, N, endpoint=False)
categories = df.index
df['radar_angles'] = theta
ax = plt.subplot(polar=True)
ax.bar(df['radar_angles'], df['pct'], width=2*pi/N, linewidth=2, edgecolor='k', alpha=0.5)
ax.set_xticks(theta)
ax.set_xticklabels(categories)
_ = ax.set_yticklabels([])
I had previously work with rose or polar bar chart. Here is the example.
import plotly.express as px
df = px.data.wind()
fig = px.bar_polar(df, r="frequency", theta="direction",
color="strength", template="plotly_dark",
color_discrete_sequence= px.colors.sequential.Plasma_r)
fig.show()
Related
Simple data as below and I want to put them in a scatter plot.
It goes well if there's not outliers (i.e. extremely big numbers).
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
dates = ["2021-01-01",
"2021-01-01", "2021-01-06",
"2021-01-08", "2021-01-12",
"2021-02-01", "2021-02-11",
"2021-02-12", "2021-02-15",
"2021-02-16", "2021-03-11",
"2021-03-21", "2021-03-22",
"2021-03-23", "2021-03-24",
"2021-04-02", "2021-04-12",
"2021-04-22", "2021-04-26",
"2021-04-30"]
numbers= [6400,
5100,5000,
4000,3686,
9000,8050,
8000,6050,
6000,9000,
8500,7800,
7000,6000,
10000,9600,
8000,7883,
6686]
dates = [pd.to_datetime(d) for d in dates]
plt.scatter(dates, numbers, s =100, c = 'red')
plt.show()
But when there are one or more extreme numbers, for example the last number 6686 became 66860. The new plot shows most the scatters insignificant (because of the the new y-axis).
What's the good solution to have a scatter plot as before (keeping the y-axis as it was), and still visualizing the extreme numbers?
The purpose of the chart is show and focus the distribution of the scatters under 10000, and also note there are extreme numbers.
If you don't want to use a log scale, you can break the plot in two (or more) and plot the values below/above a threshold:
df = pd.DataFrame({'num': numbers}, index=dates)
thresh = 12000
f, (ax1, ax2) = plt.subplots(nrows=2, sharex=True,
gridspec_kw={'height_ratios': (1,3)},
figsize=(10,4)
)
lows = df.mask(df['num'].ge(thresh))
highs = df.mask(df['num'].lt(thresh))
ax2.scatter(df.index, lows)
ax1.scatter(df.index, highs)
output:
I have this dataframe and I want to line plot it. As I have plotted it.
Graph is
Code to generate is
fig, ax = plt.subplots(figsize=(15, 5))
date_time = pd.to_datetime(df.Date)
df = df.set_index(date_time)
plt.xticks(rotation=90)
pd.DataFrame(df, columns=df.columns).plot.line( ax=ax,
xticks=pd.to_datetime(frame.Date))
I want a marker of innovationScore with value(where innovationScore is not 0) on open, close line. I want to show that that is the change when InnovationScore changes.
You have to address two problems - marking the corresponding spots on your curves and using the dates on the x-axis. The first problem can be solved by identifying the dates, where the score is not zero, then plotting markers on top of the curve at these dates. The second problem is more of a structural nature - pandas often interferes with matplotlib when it comes to date time objects. Using pandas standard plotting functions is good because it addresses common problems. But mixing pandas with matplotlib plotting (and to set the markers, you have to revert to matplotlib afaik) is usually a bad idea because they do not necessarily present the date time in the same format.
import pandas as pd
from matplotlib import pyplot as plt
#fake data generation, the following code block is just for illustration
import numpy as np
np.random.seed(1234)
n = 50
date_range = pd.date_range("20180101", periods=n, freq="D")
choice = np.zeros(10)
choice[0] = 3
df = pd.DataFrame({"Date": date_range,
"Open": np.random.randint(100, 150, n),
"Close": np.random.randint(100, 150, n),
"Innovation Score": np.random.choice(choice, n)})
fig, ax = plt.subplots()
#plot the three curves
l = ax.plot(df["Date"], df[["Open", "Close", "Innovation Score"]])
ax.legend(iter(l), ["Open", "Close", "Innovation Score"])
#filter dataset for score not zero
IS = df[df["Innovation Score"] > 0]
#plot markers on these positions
ax.plot(IS["Date"], IS[["Open", "Close"]], "ro")
#and/or set vertical lines to indicate the position
ax.vlines(IS["Date"], 0, max(df[["Open", "Close"]].max()), ls="--")
#label x-axis score not zero
ax.set_xticks(IS["Date"])
#beautify the output
ax.set_xlabel("Month")
ax.set_ylabel("Artifical score people take seriously")
fig.autofmt_xdate()
plt.show()
Sample output:
You can achieve it like this:
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], "ro-") # r is red, o is for larger marker, - is for line
plt.plot([3, 2, 1], "b.-") # b is blue, . is for small marker, - is for line
plt.show()
Check out also example here for another approach:
https://matplotlib.org/3.3.3/gallery/lines_bars_and_markers/markevery_prop_cycle.html
I very often get inspiration from this list of examples:
https://matplotlib.org/3.3.3/gallery/index.html
I am working on a dashboard using Altair. I am creating 4 different plot using the same data. I am creating scatterplots using mark_circle.
How do I change the size to be size*2, or anything else?
Here is a sample:
bar = alt.Chart(df).mark_point(filled=True).encode(
x='AGE_GROUP:N',
y=alt.Y( 'PERP:N', axis=alt.Axis( values= df['PERP'].unique().tolist() )),
size = 'count()')
You can do this by adjusting the scale range for the size encoding. For example, this sets the range such that the smallest points have an area of 100 square pixels, and the largest have an area of 500 square pixels:
import altair as alt
import pandas as pd
import numpy as np
df = pd.DataFrame({
'x': np.random.randn(30),
'y': np.random.randn(30),
'count': np.random.randint(1, 5, 30)
})
alt.Chart(df).mark_point().encode(
x='x',
y='y',
size=alt.Size('count', scale=alt.Scale(range=[100, 500]))
)
I have several histograms that I succeded to plot using plotly like this:
fig.add_trace(go.Histogram(x=np.array(data[key]), name=self.labels[i]))
I would like to create something like this 3D stacked histogram but with the difference that each 2D histogram inside is a true histogram and not just a hardcoded line (my data is of the form [0.5 0.4 0.5 0.7 0.4] so using Histogram directly is very convenient)
Note that what I am asking is not similar to this and therefore also not the same as this. In the matplotlib example, the data is presented directly in a 2D array so the histogram is the 3rd dimension. In my case, I wanted to feed a function with many already computed histograms.
The snippet below takes care of both binning and formatting of the figure so that it appears as a stacked 3D chart using multiple traces of go.Scatter3D and np.Histogram.
The input is a dataframe with random numbers using np.random.normal(50, 5, size=(300, 4))
We can talk more about the other details if this is something you can use:
Plot 1: Angle 1
Plot 2: Angle 2
Complete code:
# imports
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = 'browser'
# data
np.random.seed(123)
df = pd.DataFrame(np.random.normal(50, 5, size=(300, 4)), columns=list('ABCD'))
# plotly setup
fig=go.Figure()
# data binning and traces
for i, col in enumerate(df.columns):
a0=np.histogram(df[col], bins=10, density=False)[0].tolist()
a0=np.repeat(a0,2).tolist()
a0.insert(0,0)
a0.pop()
a1=np.histogram(df[col], bins=10, density=False)[1].tolist()
a1=np.repeat(a1,2)
fig.add_traces(go.Scatter3d(x=[i]*len(a0), y=a1, z=a0,
mode='lines',
name=col
)
)
fig.show()
Unfortunately you can't use go.Histogram in a 3D space so you should use an alternative way. I used go.Scatter3d and I wanted to use the option to fill line doc but there is an evident bug see
import numpy as np
import plotly.graph_objs as go
# random mat
m = 6
n = 5
mat = np.random.uniform(size=(m,n)).round(1)
# we want to have the number repeated
mat = mat.repeat(2).reshape(m, n*2)
# and finally plot
x = np.arange(2*n)
y = np.ones(2*n)
fig = go.Figure()
for i in range(m):
fig.add_trace(go.Scatter3d(x=x,
y=y*i,
z=mat[i,:],
mode="lines",
# surfaceaxis=1 # bug
)
)
fig.show()
Following up my previous question: Sorting datetime objects by hour to a pandas dataframe then visualize to histogram
I need to plot 3 bars for one X-axis value representing viewer counts. Now they show those under one minute and above. I need one showing the overall viewers. I have the Dataframe but I can't seem to make them look right. With just 2 bars I have no problem, it looks just like I would want it with two bars:
The relevant part of the code for this:
# Time and date stamp variables
allviews = int(df['time'].dt.hour.count())
date = str(df['date'][0].date())
hours = df_hist_short.index.tolist()
hours[:] = [str(x) + ':00' for x in hours]
The hours variable that I use to represent the X-axis may be problematic, since I convert it to string so I can make the hours look like 23:00 instead of just the pandas index output 23 etc. I have seen examples where people add or subtract values from the X to change the bars position.
fig, ax = plt.subplots(figsize=(20, 5))
short_viewers = ax.bar(hours, df_hist_short['time'], width=-0.35, align='edge')
long_viewers = ax.bar(hours, df_hist_long['time'], width=0.35, align='edge')
Now I set the align='edge' and the two width values are absolutes and negatives. But I have no idea how to make it look right with 3 bars. I didn't find any positioning arguments for the bars. Also I have tried to work with the plt.hist() but I couldn't get the same output as with the plt.bar() function.
So as a result I wish to have a 3rd bar on the graph shown above on the left side, a bit wider than the other two.
pandas will do this alignment for you, if you make the bar plot in one step rather than two (or three). Consider this example (adapted from the docs to add a third bar for each animal).
import pandas as pd
import matplotlib.pyplot as plt
speed = [0.1, 17.5, 40, 48, 52, 69, 88]
lifespan = [2, 8, 70, 1.5, 25, 12, 28]
height = [1, 5, 20, 3, 30, 6, 10]
index = ['snail', 'pig', 'elephant',
'rabbit', 'giraffe', 'coyote', 'horse']
df = pd.DataFrame({'speed': speed,
'lifespan': lifespan,
'height': height}, index=index)
ax = df.plot.bar(rot=0)
plt.show()
In pure matplotlib, instead of using the width parameter to position the bars as you've done, you can adjust the x-values for your plot:
import numpy as np
import matplotlib.pyplot as plt
# Make some fake data:
n_series = 3
n_observations = 5
x = np.arange(n_observations)
data = np.random.random((n_observations,n_series))
# Plotting:
fig, ax = plt.subplots(figsize=(20,5))
# Determine bar widths
width_cluster = 0.7
width_bar = width_cluster/n_series
for n in range(n_series):
x_positions = x+(width_bar*n)-width_cluster/2
ax.bar(x_positions, data[:,n], width_bar, align='edge')
In your particular case, seaborn is probably a good option. You should (almost always) try keep your data in long-form so instead of three separate data frames for short, medium and long, it is much better practice to keep a single data frame and add a column that labels each row as short, medium or long. Use this new column as the hue parameter in Seaborn's barplot