%matplotlib inline
import matplotlib
matplotlib.style.use('ggplot')
import numpy as np
import pandas as pd
my_data = np.array([[ 0.110622 , 0.98174432, 0.56583323],
[ 0.61825694, 0.14166864, 0.44180003],
[ 0.02572145, 0.55764373, 0.24183103],
[ 0.98040318, 0.76171712, 0.41994361],
[ 0.49859658, 0.76637672, 0.75487683]])
pd.DataFrame(my_data).plot(kind='bar', stacked='true')
Using the above code I get:
How do I change this so that the hight of every bar is the max value for that bar instead of the sum, and so all the lower values for the bar are in the same bar as different colors?
Thanks for your help.
If I understood well your question, I would normalize your data multiplying each value by the current maximum and then divided by the sum of all elements. So that:
df = df.apply(lambda x: x*df.max(axis=1)/df.sum(axis=1))
where:
df = pd.DataFrame(my_data)
The new plot is:
df.plot(kind='bar', stacked='true')
Hope that helps.
Related
Is there a way to plot bars which do not all start from the same baseline and have their own top value, but rather specify both a bottom and a top value?
In other words if I had the following dataframe:
import pandas as pd
data={'Seconds':[10,20,30,40],'SYS':[95,103,99,112],'DIA':[56,75,62,70]}
df = pd.DataFrame(data)
and from this I would need to show 4 bars, with the 'Seconds" values on the X axis and four bars which would start with at the df['DIA'] value and the top of the bar at the df['SYS'] value.
Is it possible? Thank you
You can do the following:
from matplotlib import pyplot
pyplot.bar(
x=df['Seconds'],
height=df['SYS'] - df['DIA'],
bottom=df['DIA'],
)
output:
I have a simple Pandas data frame with two columns, 'Angle' and 'rff'. I want to get an interpolated 'rff' value based on entering an Angle that falls between two Angle values (i.e. between two index values) in the data frame. For example, I'd like to enter 3.4 for the Angle and then get an interpolated 'rff'. What would be the best way to accomplish that?
import pandas as pd
data = [[1.0,45.0], [2,56], [3,58], [4,62],[5,70]] #Sample data
s= pd.DataFrame(data, columns = ['Angle', 'rff'])
print(s)
s = s.set_index('Angle') #Set 'Angle' as index
print(s)
result = s.at[3.0, "rff"]
print(result)
You may use numpy:
import numpy as np
np.interp(3.4, s.index, s.rff)
#59.6
You could use numpy for this:
import numpy as np
import pandas as pd
data = [[1.0,45.0], [2,56], [3,58], [4,62],[5,70]] #Sample data
s= pd.DataFrame(data, columns = ['Angle', 'rff'])
print(s)
print(np.interp(3.4, s.Angle, s.rff))
>>> 59.6
I am trying to create a stacked barplot using a data frame I have created that
looks like this
I want the stacked bar chart to show the 'types of exploitation' on the x axis, and then the male and female figures stacked on top of each other under these headings.
Is there a way to do this reading the info from my df? I have read about creating an index to do this but do not understand if this is the solution?
I also need a legend showing 'male' and 'female'
You can stack bars on top of eachother by the bottom function in matplotlib package.
Step 1: Create dataframe and import packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
d = {'male': [37,1032,1], 'female': [96,134,1]}
df = pd.DataFrame(data=d, index=['a', 'b', 'c'])
Step 2: Create graph
r = [0,1,2]
bars1 = df['female']
bars2 = df['male']
plt.bar(r, bars1)
plt.bar(r, bars2,bottom=bars1, color='#557f2d')
plt.xticks(r, df.index, fontweight='bold')
plt.legend(labels = ['female', 'male'])
plt.show()
More information could be found on this webpage: Link
My DataFrame's structure
trx.columns
Index(['dest', 'orig', 'timestamp', 'transcode', 'amount'], dtype='object')
I'm trying to plot transcode (transaction code) against amount to see the how much money is spent per transaction. I made sure to convert transcode to a categorical type as seen below.
trx['transcode']
...
Name: transcode, Length: 21893, dtype: category
Categories (3, int64): [1, 17, 99]
The result I get from doing plt.scatter(trx['transcode'], trx['amount']) is
Scatter plot
While the above plot is not entirely wrong, I would like the X axis to contain just the three possible values of transcode [1, 17, 99] instead of the entire [1, 100] range.
Thanks!
In matplotlib 2.1 you can plot categorical variables by using strings. I.e. if you provide the column for the x values as string, it will recognize them as categories.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"x" : np.random.choice([1,17,99], size=100),
"y" : np.random.rand(100)*100})
plt.scatter(df["x"].astype(str), df["y"])
plt.margins(x=0.5)
plt.show()
In order to optain the same in matplotlib <=2.0 one would plot against some index instead.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({"x" : np.random.choice([1,17,99], size=100),
"y" : np.random.rand(100)*100})
u, inv = np.unique(df["x"], return_inverse=True)
plt.scatter(inv, df["y"])
plt.xticks(range(len(u)),u)
plt.margins(x=0.5)
plt.show()
The same plot can be obtained using seaborn's stripplot:
sns.stripplot(x="x", y="y", data=df)
And a potentially nicer representation can be done via seaborn's swarmplot:
sns.swarmplot(x="x", y="y", data=df)
I'm doing a k-means clustering of activities on some open source projects on GitHub and am trying to plot the results together with the cluster centroids using Seaborn Scatterplot Matrix.
I can successfully plot the results of the clustering analysis (example tsv output below)
user_id issue_comments issues_created pull_request_review_comments pull_requests category
1 0.14936519790888722 2.0100502512562812 0.0 0.60790273556231 Group 0
1882 0.11202389843166542 0.5025125628140703 0.0 0.0 Group 1
2 2.315160567587752 20.603015075376884 0.13297872340425532 1.21580547112462 Group 2
1789 36.8185212845407 82.91457286432161 75.66489361702128 74.46808510638297 Group 3
The problem I'm having is that I'd like to be able to also plot the centroids of the clusters on the matrix plot too. Currently I'm my plotting script looks like this:
import seaborn as sns
import pandas as pd
from pylab import savefig
sns.set()
# By default, Pandas assumes the first column is an index
# so it will be skipped. In our case it's the user_id
data = pd.DataFrame.from_csv('summary_clusters.tsv', sep='\t')
grid = sns.pairplot(data, hue="category", diag_kind="kde")
savefig('normalised_clusters.png', dpi = 150)
This produces the expected output:
I'd like to be able to mark on each of these plots the centroids of the clusters. I can think of two ways to do this:
Create a new 'CENTROID' category and just plot this together with the other points.
Manually add extra points to the plots after calling sns.pairplot(data, hue="category", diag_kind="kde").
If (1) is the solution then I'd like to be able to customise the marker (perhaps a star?) to make it more prominent.
If (2) I'm all ears. I'm pretty new to Seaborn and Matplotlib so any assistance would be very welcome :-)
pairplot isn't going to be all that well suited to this sort of thing, but it's possible to make it work with a few tricks. Here's what I would do.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
sns.set_color_codes()
# Make some random iid data
cov = np.eye(3)
ds = np.vstack([np.random.multivariate_normal([0, 0, 0], cov, 50),
np.random.multivariate_normal([1, 1, 1], cov, 50)])
ds = pd.DataFrame(ds, columns=["x", "y", "z"])
# Fit the k means model and label the observations
km = KMeans(2).fit(ds)
ds["label"] = km.labels_.astype(str)
Now comes the non-obvious part: you need to create a dataframe with the centroid locations and then combine it with the dataframe of observations while identifying the centroids as appropriate using the label column:
centroids = pd.DataFrame(km.cluster_centers_, columns=["x", "y", "z"])
centroids["label"] = ["0 centroid", "1 centroid"]
full_ds = pd.concat([ds, centroids], ignore_index=True)
Then you just need to use PairGrid, which is a bit more flexible than pairplot and will allow you to map other plot attributes by the hue variable along with the color (at the expense of not being able to draw histograms on the diagonals):
g = sns.PairGrid(full_ds, hue="label",
hue_order=["0", "1", "0 centroid", "1 centroid"],
palette=["b", "r", "b", "r"],
hue_kws={"s": [20, 20, 500, 500],
"marker": ["o", "o", "*", "*"]})
g.map(plt.scatter, linewidth=1, edgecolor="w")
g.add_legend()
An alternate solution would be to plot the observations as normal then change the data attributes on the PairGrid object and add a new layer. I'd call this a hack, but in some ways it's more straightforward.
# Plot the data
g = sns.pairplot(ds, hue="label", vars=["x", "y", "z"], palette=["b", "r"])
# Change the PairGrid dataset and add a new layer
centroids = pd.DataFrame(km.cluster_centers_, columns=["x", "y", "z"])
g.data = centroids
g.hue_vals = [0, 1]
g.map_offdiag(plt.scatter, s=500, marker="*")
I know I'm a bit late to the party, but here is a generalized version of mwaskom's code to work with n clusters. Might save someone a few minutes
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
def cluster_scatter_matrix(data_norm, cluster_number):
sns.set_color_codes()
km = KMeans(cluster_number).fit(data_norm)
data_norm["label"] = km.labels_.astype(str)
centroids = pd.DataFrame(km.cluster_centers_, columns=data_norm.columns)
centroids["label"] = [str(n)+" centroid" for n in range(cluster_number)]
full_ds = pd.concat([data_norm, centroids], ignore_index=True)
g = sns.PairGrid(full_ds, hue="label",
hue_order=[str(n) for n in range(cluster_number)]+[str(n)+" centroid" for n in range(cluster_number)],
#palette=["b", "r", "b", "r"],
hue_kws={"s": [ 20 for n in range(cluster_number)]+[500 for n in range(cluster_number)],
"marker": [ 'o' for n in range(cluster_number)]+['*' for n in range(cluster_number)]}
)
g.map(plt.scatter, linewidth=1, edgecolor="w")
g.add_legend()