I was trying to plot the seaborn distribution plot for a list of columns.
for i in ['age', 'trestbps', 'chol','thalach','oldpeak', 'ca']:
sns.distplot(Data_heart_copy[i])
The output with above code
However, what I wanted to display is distplot for all the above columns in a single window with a compact command
The output that I am looking for looks like this
Required Output
You need to use subplot to put plots side by side:
import matplotlib.pyplot as plt
for i, col in enumerate(['age', 'trestbps', 'chol','thalach','oldpeak', 'ca']):
plt.subplot(2,3,i+1)
sns.distplot(Data_heart_copy[col])
plt.subplot(nrow, ncol, item) takes 3 input arguments: number of rows in the grid, number of columns, and the plot index (starting from 1 to nrow x ncol)
Related
I have a very simple data frame but I could not plot a line using a row and a column. Here is an image, I would like to plot a "line" that connects them.
enter image description here
I tried to plot it but x-axis disappeared. And I would like to swap those axes. I could not find an easy way to plot this simple thing.
Try:
import matplotlib.pyplot as plt
# Categories will be x axis, sexonds will be y
plt.plot(data["Categories"], data["Seconds"])
plt.show()
Matplotlib generates the axis dynamically, so if you want the labels of the x-axis to appear you'll have to increase the size of your plot.
I am trying to plot variable Vs SalePrice data. I tried pd.scatter_matrix but I am getting number of unnecessary plot with various combinations. I look for is SalePrice in Y axis and a scatter plot for each element from the data set. Here is the code I tried.
data_prep_num['Sales_test_data']=data_sales_price_old
att=['Sales_test_data','YearBuilt','LotArea','MSSubClass','BsmtFinSF1','TotalBsmtSF','1stFlrSF','2ndFlrSF','GrLivArea','GarageArea']
pd.scatter_matrix(data_prep_num[att],alpha=.4,figsize=(30,30))```
If you want to use pd.plotting.scatter_matrix but only want one of the rows (i.e. the Sales_test_data column), you can iterate over the plotting axes, and hide the combinations you don't want.
Assuming the SalePrice is the very first column (index 0):
import numpy as np
import matplotlib.pyplot as plt
axes = pd.plotting.scatter_matrix(data_prep_num[att], alpha=0.4, figsize=(30,30))
for i in range(np.shape(axes)[0]):
if i != 0:
for j in range(np.shape(axes)[1]):
axes[i,j].set_visible(False)
Note: This is obviously not super efficient when you start having lots of columns though.
Suppose I have dataframe, which has index composed of two columns and I want to plot it:
import pandas
from matplotlib import pyplot as plot
df=pandas.DataFrame(data={'floor':[1,1,1,2,2,2,3,3],'room':[1,2,3,1,1,2,1,3],'count':[1, 1, 3,2,2,4,1,5]})
df2=df.groupby(['floor','room']).sum()
df2.plot()
plot.show()
The above example will result in a plot where row numbers are used for x axis and no tick labels. Are there any facilities to use the index instead?
Say, I'd like to have x axis separated into even sections for first column of index and spread out points values of second index column inside those sections.
It seems like plotting a line connecting the mean values of box plots would be a simple thing to do, but I couldn't figure out how to do this plot in pandas.
I'm using this syntax to do the boxplot so that it automatically generate the box plot for Y vs. X device without having to do external manipulation of the data frame:
df.boxplot(column='Y_Data', by="Category", showfliers=True, showmeans=True)
One way I thought of doing is to just do a line plot by getting the mean values from the boxplot, but I'm not sure how to extract that information from the plot.
You can save the axis object that gets returned from df.boxplot(), and plot the means as a line plot using that same axis. I'd suggest using Seaborn's pointplot for the lines, as it handles a categorical x-axis nicely.
First let's generate some sample data:
import pandas as pd
import numpy as np
import seaborn as sns
N = 150
values = np.random.random(size=N)
groups = np.random.choice(['A','B','C'], size=N)
df = pd.DataFrame({'value':values, 'group':groups})
print(df.head())
group value
0 A 0.816847
1 A 0.468465
2 C 0.871975
3 B 0.933708
4 A 0.480170
...
Next, make the boxplot and save the axis object:
ax = df.boxplot(column='value', by='group', showfliers=True,
positions=range(df.group.unique().shape[0]))
Note: There's a curious positions argument in Pyplot/Pandas boxplot(), which can cause off-by-one errors. See more in this discussion, including the workaround I've employed here.
Finally, use groupby to get category means, and then connect mean values with a line plot overlaid on top of the boxplot:
sns.pointplot(x='group', y='value', data=df.groupby('group', as_index=False).mean(), ax=ax)
Your title mentions "median" but you talk about category means in your post. I used means here; change the groupby aggregation to median() if you want to plot medians instead.
You can get the value of the medians by using the .get_data() property of the matplotlib.lines.Line2D objects that draw them, without having to use seaborn.
Let bp be your boxplot created as bp=plt.boxplot(data). Then, bp is a dict containing the medians key, among others. That key contains a list of matplotlib.lines.Line2D, from which you can extract the (x,y) position as follows:
bp=plt.boxplot(data)
X=[]
Y=[]
for m in bp['medians']:
[[x0, x1],[y0,y1]] = m.get_data()
X.append(np.mean((x0,x1)))
Y.append(np.mean((y0,y1)))
plt.plot(X,Y,c='C1')
For an arbitrary dataset (data), this script generates this figure. Hope it helps!
I am trying to generate a grid of subplots based off of a Pandas groupby object. I would like each plot to be based off of two columns of data for one group of the groupby object. Fake data set:
C1,C2,C3,C4
1,12,125,25
2,13,25,25
3,15,98,25
4,12,77,25
5,15,889,25
6,13,56,25
7,12,256,25
8,12,158,25
9,13,158,25
10,15,1366,25
I have tried the following code:
import pandas as pd
import csv
import matplotlib as mpl
import matplotlib.pyplot as plt
import math
#Path to CSV File
path = "..\\fake_data.csv"
#Read CSV into pandas DataFrame
df = pd.read_csv(path)
#GroupBy C2
grouped = df.groupby('C2')
#Figure out number of rows needed for 2 column grid plot
#Also accounts for odd number of plots
nrows = int(math.ceil(len(grouped)/2.))
#Setup Subplots
fig, axs = plt.subplots(nrows,2)
for ax in axs.flatten():
for i,j in grouped:
j.plot(x='C1',y='C3', ax=ax)
plt.savefig("plot.png")
But it generates 4 identical subplots with all of the data plotted on each (see example output below):
I would like to do something like the following to fix this:
for i,j in grouped:
j.plot(x='C1',y='C3',ax=axs)
next(axs)
but I get this error
AttributeError: 'numpy.ndarray' object has no attribute 'get_figure'
I will have a dynamic number of groups in the groupby object I want to plot, and many more elements than the fake data I have provided. This is why I need an elegant, dynamic solution and each group data set plotted on a separate subplot.
Sounds like you want to iterate over the groups and the axes in parallel, so rather than having nested for loops (which iterates over all groups for each axis), you want something like this:
for (name, df), ax in zip(grouped, axs.flat):
df.plot(x='C1',y='C3', ax=ax)
You have the right idea in your second code snippet, but you're getting an error because axs is an array of axes, but plot expects just a single axis. So it should also work to replace next(axs) in your example with ax = axs.next() and change the argument of plot to ax=ax.