I have a dataset and I want to find out how several columns values (numeric values) differ across two different groups ('group' is a column that takes either the value of 'high' or 'low').
I want to plot several barplots using a similar system/aesthetics to Seaborn's FacetGrid or PairGrid. Each plot will have a different Y value but the same X-axis (The group variable)
This is what I have so far:
sns.catplot(x='group', y='Number of findings (total)', kind="bar",
palette="muted", data=df)
But I would like to write a loop that can replace my y variable with different variables. How to do it?
Related
I have a dataframe with two columns, the first one can have an integer from 0-15, the other one can have an integer from 0-10.
The df has approximately 10,000 rows.
I want to plot some sort of grid, (15x10) that can visually represent how many instances of each combination I have throughout the dataframe, ideally displaying the actual number on every grid cell.
I have tried both Seaborn and Matplotlib.
In Seaborn I tried a jointplot which almost did it but I can't get it to show an actual 15x10 grid. I also tried a heatmap but it gave me an error (see below) and I wasn't able to find anything on it.
I also tried plotting some sort of 3D histogram.
Finally I tried pivoting the data but Pandas calculates the numbers as values instead of treating them as "buckets".
Not sure where to go from here.
*heatmap error: "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
sns.heatmap(x='pressure_bucket', y='rate_bucket', data=df)
The closest to what I want is something like this, ideally with the actual numbers in each cell
https://imgur.com/a/d4qWIod
Thanks to all in advance!
We can use plt.imshow to display a heat map,
# get the counts in form of a dataframe indexed by (c1,c2)
counts = df.groupby(['c1'])['c2'].value_counts().rename('value').reset_index()
# pivot to c1 as index, c2 as columns
counts = counts.pivot(index='c1', columns='c2', values='value')
# after reading your question carefully, there's another step
# fill all missing value in c1
counts.reindex(range(16))
# fill all missing value in c2
counts = counts.reindex(range(10), axis=1)
# fill all missing values with 0
counts = counts.fillna(0)
# imshow
plt.figure(figsize=(15,10))
plt.imshow(counts, cmap='hot')
plt.grid(False)
plt.show()
# sns would give a color bar legend
plt.figure(figsize=(15,10))
sns.heatmap(counts, cmap='hot')
plt.show()
Output (random entries)
Output sns:
I have a data frame called 'train' with a column 'string' and a column 'string length' and a column 'rank' which has ranking ranging from 0-4.
I want to create a histogram of the string length for each ranking and plot all of the histograms on one graph to compare. I am experiencing two issues with this:
The only way I can manage to do this is by creating separate datasets e.g. with the following type of code:
S0 = train.loc[train['rank'] == 0]
S1 = train.loc[train['rank'] == 1]
Then I create individual histograms for each dataset using:
plt.hist(train['string length'], bins = 100)
plt.show()
This code doesn't plot the density but instead plots the counts. How do I alter my code such that it plots density instead?
Is there also a way to do this without having to create separate datasets? I was told that my method is 'unpythonic'
You could do something like:
df.loc[:, df.columns != 'string'].groupby('rank').hist(density=True, bins =10, figsize=(5,5))
Basically, what it does is select all columns except string, group them by rank and make an histogram of all them following the arguments.
The density argument set to density=True draws it in a normalized manner, as
Hope this has helped.
EDIT:
f there are more variables and you want the histograms overlapped, try:
df.groupby('rank')['string length'].hist(density=True, histtype='step', bins =10,figsize=(5,5))
I am trying out Seaborn to make my plot visually better than matplotlib. I have a dataset which has a column 'Year' which I want to plot on the X-axis and 4 Columns say A,B,C,D on the Y-axis using different coloured lines. I was trying to do this using the sns.lineplot method but it allows for only one variable on the X-axis and one on the Y-axis. I tried doing this
sns.lineplot(data_preproc['Year'],data_preproc['A'], err_style=None)
sns.lineplot(data_preproc['Year'],data_preproc['B'], err_style=None)
sns.lineplot(data_preproc['Year'],data_preproc['C'], err_style=None)
sns.lineplot(data_preproc['Year'],data_preproc['D'], err_style=None)
But this way I don't get a legend in the plot to show which coloured line corresponds to what. I tried checking the documentation but couldn't find a proper way to do this.
Seaborn favors the "long format" as input. The key ingredient to convert your DataFrame from its "wide format" (one column per measurement type) into long format (one column for all measurement values, one column to indicate the type) is pandas.melt. Given a data_preproc structured like yours, filled with random values:
num_rows = 20
years = list(range(1990, 1990 + num_rows))
data_preproc = pd.DataFrame({
'Year': years,
'A': np.random.randn(num_rows).cumsum(),
'B': np.random.randn(num_rows).cumsum(),
'C': np.random.randn(num_rows).cumsum(),
'D': np.random.randn(num_rows).cumsum()})
A single plot with four lines, one per measurement type, is obtained with
sns.lineplot(x='Year', y='value', hue='variable',
data=pd.melt(data_preproc, ['Year']))
(Note that 'value' and 'variable' are the default column names returned by melt, and can be adapted to your liking.)
This:
sns.lineplot(data=data_preproc)
will do what you want.
See the documentation:
sns.lineplot(x="Year", y="signal", hue="label", data=data_preproc)
You probably need to re-organize your dataframe in a suitable way so that there is one column for the x data, one for the y data, and one which holds the label for the data point.
You can also just use matplotlib.pyplot. If you import seaborn, much of the improved design is also used for "regular" matplotlib plots. Seaborn is really "just" a collection of methods which conveniently feed data and plot parameters to matplotlib.
I have a pandas df with multiple columns. I want to scatter plot multiple ordinary least squares regressions with equations showing on the plot. I also want to vary based on another column or 'hue' so that certain column values or hue's are plotted separately.
sns.relplot(y='Latitude', x='Longitude', hue='cluster', palette="Paired", s=9, data=df)
Is there a streamlined way of doing this, so that you don't have to write a function to iterate over each column and hue?
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!