seaborn | Statistical data visualization in Python | Data Visualization library
kandi X-RAY | seaborn Summary
kandi X-RAY | seaborn Summary
Statistical data visualization in Python
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Top functions reviewed by kandi - BETA
- Plot a scatter plot
- The legend
- Draw a matplotlib figure
- Add a legend
- Combine the data into a single plot
- Generate a joint -man plot
- Inject kwargs into kwargs
- Plot marginal properties for a function
- Apply func to func
- Plot a lmplot
- Show a dark palette
- Show a light palette
- Plot the marginal properties of a function
- Determine the colormap parameters
- Generate a swarm plot
- Choose the diverging palette
- Call func on func
- Extract the docstring from the file
- Get the plot data
- Combine the data into a new plot
- Choose a cubehelix palette
- Load an example dataset
- Plot clusters of data
- Move a legend
- Plot the residual data
- Set title and col_titles
- Draw a logo
- Chooses a colorbrewer palette
seaborn Key Features
seaborn Examples and Code Snippets
Lines of Code : 21License : Permissive (MIT License)
Copydef load_data(dataset_name: str, cola_name: str, colb_name: str) -> np.mat:
"""
Function used for loading data from the seaborn splitting into x and y points
>>> pass # this function has no doctest
"""
import seaborn a print(df)
# out:
Data Mean sd time__1 time__2 time__3 time__4 \
0 Data_1 0.947667 0.025263 0.501517 0.874750 0.929426 0.953847
1 Data_2 0.031960 0.017314 0.377588 0.069185 0.037523 0.024028
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
genres = [['action', 'drama', 'comedy'], ['comedy', 'drama'], ['action', 'sci-fi', 'comedy']]
rating = [6, 8, 3]
data = {'Rating': rating, 'Genres': genres}
df = pfilt_a= df_a.loc[df_a['Category'] == cat].reset_index()
# The x-coordinates are not 2020, 2021, 2022.
# They are 0, 1, 2. The *x-labels* are 2020, 2021, 2022
sns.barplot(data=filt_q, x='Year', y='value', ...)
# Thimport seaborn as sns
sns.barplot(data=df, x='player', y='wins', hue='position', dodge=False,
palette=dict(zip(df['position'], df['color'])))
import seaborn as sns
df = (hap_groups
.reset_index(name='count')
.assign(generation=lambda d: d['Generation'].str.extract('^(\d+)').astype(int))
)
sns.lineplot(data=df, x='generation', y='count', hue='Line', marker='o')
<cityScatter.plot(kind='scatter', x='longitude', y='latitude', c='country')
['2' '3' '4' '5more' nan]
`data.dropna(inplace=True)`
`data = data.dropna()`
sns.jointplot(x='sepal_length', y='sepal_width', data=sns.load_dataset('iris'),
kind='kde', cmap='plasma', space=0)
# -------
sns.histplot(
data=melt, y='Time', hue='variable', weights='value',
multiple='stack', shrink=0.8, discrete=True,
)
Community Discussions
Trending Discussions on seaborn
QUESTION
I am working on the Kaggle: Abalone dataset and I am facing a weird problem when plotting a boxplot.
...ANSWER
Answered 2022-Mar-10 at 10:38If you want a box plot per value of a categorical column I suggest:
QUESTION
How do you size the axes of a marginal plot to match the size of a non-square central plot using matplotlib?
In the image, you'll see that the top marginal plot is too wide, even though it shares the x-axis labels.
Context: I'm trying to create a joint plot like in Seaborn, but with a non-square heatmap at center and bar graphs as the marginal plots. JointGrids isn't designed to work with heatmaps (which is okay, on to matplotlib!). Merging a matplotlib heatmap with subplot barplots gets me close, but I find one bargraph's axis is larger than the central heatmap even when I share axes.
Minimum working example:
...ANSWER
Answered 2022-Feb-15 at 01:17As the heatmap gets a default "equal" aspect ratio, and gets shrunk due to the colorbar, an idea is to manually resize the histograms once everything is created.
QUESTION
I have an excel file with a series of formatted charts on a tab called Charts. I have named the charts, Figure1, Figure2, Figure3, etc.
I have an existing PowerPoint template. The template has 2 placeholders per slide (so that it can accommodate 2 charts per slide).
I would like to paste Figure1 in the left placeholder of slide 3, and Figure2 in the right placeholder of slide 3. I want to do this in python as the data analysis is done in python and excel is used to share stored results with colleagues.
Attempt 1:
Attempt 1 uses win32com.client. I am following this example: How to copy chart from excel and paste it as chart into powerpoint (not image) using python
but I cannot get the syntax right to insert the chart into the placeholder. When I follow the syntax in the solution, nothing happens and I get a message
>
Current code:
...ANSWER
Answered 2022-Feb-12 at 02:55You're very close! Copy and Paste are methods, so to call them you need to add brackets after them, e.g. Copy().
To get slide 2, you need to use the Item method of the Slides class: ppt.Slides.Item(2)
QUESTION
I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.
But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.
So I did the following:
- write a simple
meta.yamlso I could use conda-build to buildjivelocally - build jive with
conda build . - I looked at the tarball that was produced and it does indeed contain the
jivesource as expected - In my other project, add jive to the dependencies in
environment.yml, and add 'local' to the list of channels. - create a conda env using that environment.yml.
When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.)
How can I fix the build/install so that this works?
Here is the meta.yaml, which lives in the jive project top level directory:
ANSWER
Answered 2022-Feb-05 at 04:16The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.
I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).
Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").
Try adjusting the meta.yaml to something like
QUESTION
I want to add the median and IQ values to the violin plot. However, I didn't find its argument.
...ANSWER
Answered 2022-Jan-14 at 18:59If you look at the lines in ax you can see that they contain the coordinates of the quartile and median lines (see below)
So we could just take the non-zero element from the first array in each line, and the first element of the second array to get the x and y, and use the y as the text value.
QUESTION
I am working with a simple ML model with streamlit. It runs fine on my local machine inside conda environment, but it shows Error installing requirements when I try to deploy it on share.streamlit.io.
The error message is the following:
ANSWER
Answered 2021-Dec-25 at 14:42Streamlit share runs the app in a linux environment meaning there is no pywin32 because this is for windows.
Delete the pywin32 from the requirements file and also the pywinpty==1.1.6 for the same reason.
After deleting these requirements re-deploy your app and it will work.
QUESTION
For pandas agg, is there a way to specify the aggregation function based on the data type? For example, all columns of type object get "first", all floats get "mean", and so on? So as to avoid having to type out all the columns with their respective aggregating functions.
Sample data:
...ANSWER
Answered 2021-Dec-16 at 12:25def a(x):
if x.dtype == np.dtype('float64'):
dict[x.name] = "mean"
elif x.dtype == np.dtype('object'):
dict[x.name] = "first"
dict = {}
df = df.apply(a)
iris.agg(dict)
QUESTION
I have this issue with heatmap from seaborn. I don't know how, but seaborn.heatmap() refuses to take in dataframe, it instead show the mentioned error. Seaborn, matplotlib and pandas is up-to-date and I'm using python 3.10 on Visual Studio. The code is just a sample code from seaborn.heatmap itself:
...ANSWER
Answered 2021-Dec-05 at 09:37QUESTION
I have a df
ANSWER
Answered 2021-Dec-03 at 09:52Use cut with crosstab and add DataFrame.add_prefix:
QUESTION
Using Matplotlib to make a scatter plot (not Seaborn, Pandas, or other high-level interface), how can I use a dictionary to specify marker types?
This example works with a color dictionary:
...ANSWER
Answered 2021-Dec-02 at 18:42Based on the comments by @BigBen, it looks like Matplotlib doesn't support multiple markers. @BigBen linked to a couple of example work-arounds, but I found the following works best for me because it allows me to explicitly relate a keyword to a marker style at the beginning of my code, regardless of what subset of df I am working with. (Real-life data has a dozen "name" values, and I am working with various and mixed subsets based on attributes in other columns.)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install seaborn
You can use seaborn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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