seaborn | Statistical data visualization in Python | Data Visualization library

by mwaskom Python Version: 0.12.2 License: BSD-3-Clause

X-Ray Key Features Code Snippets(10)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | seaborn Summary

seaborn is a Python library typically used in Analytics, Data Visualization, Pandas applications. seaborn has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However seaborn build file is not available. You can install using 'pip install seaborn' or download it from GitHub, PyPI.

Statistical data visualization in Python

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seaborn has a highly active ecosystem.

It has 10480 star(s) with 1716 fork(s). There are 254 watchers for this library.

There were 2 major release(s) in the last 6 months.

There are 102 open issues and 2169 have been closed. On average issues are closed in 86 days. There are 11 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of seaborn is 0.12.2

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seaborn has 0 bugs and 0 code smells.

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seaborn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

seaborn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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seaborn is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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seaborn saves you 13118 person hours of effort in developing the same functionality from scratch.

It has 26955 lines of code, 1355 functions and 102 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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Top functions reviewed by kandi - BETA

kandi has reviewed seaborn and discovered the below as its top functions. This is intended to give you an instant insight into seaborn implemented functionality, and help decide if they suit your requirements.

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

Get all kandi verified functions for this library.

seaborn Key Features

Statistical data visualization in Python

seaborn Examples and Code Snippets

Load data from seaborn .

python

Lines of Code : 21

License : Permissive (MIT License)

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                                    def 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 as sns data = sns.load_dataset(dataset_name) col_a = np.array(data[cola_name]) # total_bill col_b = np.array(data[colb_name]) # tip mcol_a = np.mat(col_a) mcol_b = np.mat(col_b) m = np.shape(mcol_b)[1] one = np.ones((1, m), dtype=int) # horizontal stacking training_data_x = np.hstack((one.T, mcol_a.T)) return training_data_x, mcol_b, col_a, col_b

seaborn joint plot, how to remove space between marginal axes

Python

Lines of Code : 4

License : Strong Copyleft (CC BY-SA 4.0)

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sns.jointplot(x='sepal_length', y='sepal_width', data=sns.load_dataset('iris'),
              kind='kde', cmap='plasma', space=0)
#                                        -------

How to make a horizontal stacked histplot based on counts?

Python

Lines of Code : 5

License : Strong Copyleft (CC BY-SA 4.0)

Copy

sns.histplot(
    data=melt, y='Time', hue='variable', weights='value',
    multiple='stack', shrink=0.8, discrete=True,
)

How to create a seaborn violinplot of a feature that take more than one value?

Python

Lines of Code : 14

License : Strong Copyleft (CC BY-SA 4.0)

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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 = pd.DataFrame(data=data)

df = df.explode("Genres")
sns.violinplot(x='Genres', y='Rating', data=df)
plt.show()

pandas groupby().size(), to excel line graph

Python

Lines of Code : 7

License : Strong Copyleft (CC BY-SA 4.0)

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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')

Creating a Scatter Plot with different colors for a certain column

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

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cityScatter.plot(kind='scatter', x='longitude', y='latitude', c='country')

ValueError: Found unknown categories [nan] in column 2 during fit

Python

Lines of Code : 6

License : Strong Copyleft (CC BY-SA 4.0)

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['2' '3' '4' '5more' nan]

`data.dropna(inplace=True)`

`data = data.dropna()`

How to plot multiple time series from a CSV while the data points are in different columns

Python

Lines of Code : 22

License : Strong Copyleft (CC BY-SA 4.0)

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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   

    time__5  
0  0.958375  
1  0.021532

df.drop(["Mean", "sd"], axis=1).set_index("Data").T

Data       Data_1    Data_2
time__1  0.501517  0.377588
time__2  0.874750  0.069185
time__3  0.929426  0.037523
time__4  0.953847  0.024028
time__5  0.958375  0.021532

df.plot()

How to make combo charts inside subplots using a For loop in Python

Python

Lines of Code : 10

License : Strong Copyleft (CC BY-SA 4.0)

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filt_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', ...)

# The x-coordinates are the index of the filt_a dataframe
# Calling reset_index resets it to 0, 1, 2, ...
sns.lineplot(data=filt_a['Amount'], ...)

use different data for legend matplotlib

Python

Lines of Code : 5

License : Strong Copyleft (CC BY-SA 4.0)

Copy

import seaborn as sns

sns.barplot(data=df, x='player', y='wins', hue='position', dodge=False,
            palette=dict(zip(df['position'], df['color'])))

Community Discussions

Trending Discussions on seaborn

Getting Error 0 when plotting boxplot of a filtered dataset

Resize axes of top and right joint marginal plots to match central plot with matplotlib

How to paste an Excel chart into PowerPoint placeholder using Python?

How to install local package with conda

How to add median and IQR to seaborn violinplot

Can't deploy streamlit app on share.streamlit.io

Pandas agg define metric based on data type

Heatmap error :'NoneType' object is not callable when using with dataframe

Adding columns & values per group occurrence in pandas after filtering

Matplotlib scatter plot marker type from dictionary

Trending Discussions on seaborn

Getting Error 0 when plotting boxplot of a filtered dataset

Resize axes of top and right joint marginal plots to match central plot with matplotlib

How to paste an Excel chart into PowerPoint placeholder using Python?

How to install local package with conda

How to add median and IQR to seaborn violinplot

Can't deploy streamlit app on share.streamlit.io

Pandas agg define metric based on data type

Heatmap error :'NoneType' object is not callable when using with dataframe

Adding columns & values per group occurrence in pandas after filtering

Matplotlib scatter plot marker type from dictionary

QUESTION

Getting Error 0 when plotting boxplot of a filtered dataset

Asked 2022-Mar-11 at 15:48

I am working on the Kaggle: Abalone dataset and I am facing a weird problem when plotting a boxplot.

import pandas as pd
import seaborn as sns

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data', header=None)
df.columns = ['sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'rings']

If a run:

plt.figure(figsize=(16,6))
plt.subplot(121)
sns.boxplot(data=df['rings'])

working perfectly!

If I filter the dataset by sex like this:

df_f = df[df['sex']=='F']
df_m = df[df['sex']=='M']
df_i = df[df['sex']=='I']

df_f = (1307,9), df_m=(1528,9) and df_i=(1342,9)

And I run:

plt.figure(figsize=(16,6))
plt.subplot(121)
sns.boxplot(data=df_m['rings'])

working perfectly!

But if I run the code above for df_f and df_i datasets I get an error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3360             try:
-> 3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:

~/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

~/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 0

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_434828/3363262611.py in 
----> 1 sns.boxplot(data=df_f['Rings'])

~/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

~/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py in boxplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)
   2241 ):
   2242 
-> 2243     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,
   2244                           orient, color, palette, saturation,
   2245                           width, dodge, fliersize, linewidth)

~/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py in __init__(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)
    404                  width, dodge, fliersize, linewidth):
    405 
--> 406         self.establish_variables(x, y, hue, data, orient, order, hue_order)
    407         self.establish_colors(color, palette, saturation)
    408 

~/anaconda3/lib/python3.9/site-packages/seaborn/categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
     96                 if hasattr(data, "shape"):
     97                     if len(data.shape) == 1:
---> 98                         if np.isscalar(data[0]):
     99                             plot_data = [data]
    100                         else:

~/anaconda3/lib/python3.9/site-packages/pandas/core/series.py in __getitem__(self, key)
    940 
    941         elif key_is_scalar:
--> 942             return self._get_value(key)
    943 
    944         if is_hashable(key):

~/anaconda3/lib/python3.9/site-packages/pandas/core/series.py in _get_value(self, label, takeable)
   1049 
   1050         # Similar to Index.get_value, but we do not fall back to positional
-> 1051         loc = self.index.get_loc(label)
   1052         return self.index._get_values_for_loc(self, loc, label)
   1053 

~/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:
-> 3363                 raise KeyError(key) from err
   3364 
   3365         if is_scalar(key) and isna(key) and not self.hasnans:

KeyError: 0

There's no missing values, all values are int.

What am I missing here?

ANSWER

Answered 2022-Mar-10 at 10:38

If you want a box plot per value of a categorical column I suggest:

sns.boxplot(data=df, x='rings', y='sex')

Source https://stackoverflow.com/questions/71422431

QUESTION

Resize axes of top and right joint marginal plots to match central plot with matplotlib

Asked 2022-Feb-15 at 01:17

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:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

### Function from matplotlib ################################################
def heatmap(data, row_labels, col_labels, ax=None,
            cbar_kw={}, cbarlabel="", **kwargs):
    """
    Create a heatmap from a numpy array and two lists of labels.

    Parameters
    ----------
    data
        A 2D numpy array of shape (M, N).
    row_labels
        A list or array of length M with the labels for the rows.
    col_labels
        A list or array of length N with the labels for the columns.
    ax
        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If
        not provided, use current axes or create a new one.  Optional.
    cbar_kw
        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.
    cbarlabel
        The label for the colorbar.  Optional.
    **kwargs
        All other arguments are forwarded to `imshow`.
    """

    if not ax:
        ax = plt.gca()

    # Plot the heatmap
    im = ax.imshow(data, **kwargs)

    # Create colorbar
    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")

    # Show all ticks and label them with the respective list entries.
    ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)
    ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
             rotation_mode="anchor")

    # Turn spines off and create white grid.
    ax.spines[:].set_visible(False)

    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
    ax.tick_params(which="minor", bottom=False, left=False)

    return im, cbar

### Now this specific case ################################################

# Make dataframe
dd = {f'Col_{col}': [col*x**2 for x in range(25)] for col in range(16)}
index = [f'Row_{x}' for x in range(25)]
df = pd.DataFrame(dd, index=index)

# Make means by axis
ax0_means = df.mean(axis=0)
ax1_means = df.mean(axis=1)

# Build figure and axes
fig, axs = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(16,16),
    gridspec_kw=dict(height_ratios=[1, 3],width_ratios=[3, 1]))
axs[0, 1].set_visible(False)
axs[0, 0].set_box_aspect(1/3)
axs[1, 1].set_box_aspect(3/1)

# Plot data
im, cbar = heatmap(df, df.index, df.columns, ax=axs[1,0])
plt.setp(axs[1,0].get_xticklabels(), rotation=45, ha="right",
     rotation_mode="anchor")

# Rotate the tick labels and set their alignment.
axs[1, 1].barh(y=ax1_means.index, width=ax1_means.values)
axs[0, 0].bar(x=ax0_means.index, height=ax0_means.values)
plt.show()

ANSWER

Answered 2022-Feb-15 at 01:17

As 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.

from matplotlib.transforms import Bbox

# code added at the end, just before plt.show()
(x0m, y0m), (x1m, y1m) = axs[1, 0].get_position().get_points()  # main heatmap
(x0h, y0h), (x1h, y1h) = axs[0, 0].get_position().get_points()  # horizontal histogram
axs[0, 0].set_position(Bbox([[x0m, y0h], [x1m, y1h]]))
(x0v, y0v), (x1v, y1v) = axs[1, 1].get_position().get_points()  # vertical histogram
axs[1, 1].set_position(Bbox([[x0v, y0m], [x1v, y1m]]))

plt.show()

(The following example uses hspace=0.01, wspace=0.02 in the gridspec_kw=)

Source https://stackoverflow.com/questions/71119762

QUESTION

How to paste an Excel chart into PowerPoint placeholder using Python?

Asked 2022-Feb-12 at 02:55

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:

xlApp = win32.Dispatch('Excel.Application')
wb = xlApp.Workbooks.Open(outputPath+'Chart Pack.xlsb')
        
pptApp = win32.Dispatch('PowerPoint.Application')
ppt = pptApp.Presentations.Open(template)
        
# attempt 1
wb.sheets('Charts').ChartObjects('Figure1').Copy
ppt.slides[2].Shapes.Paste
    
# attempt 2
wb.sheets('Charts').ChartObjects('Figure1').Copy
ppt.slides[2].placeholders[1].Paste

Attempt 2:

Attempt 2 uses python-pptx. I looked at the documentation here: https://python-pptx.readthedocs.io/en/latest/user/placeholders-using.html

but the example involves creating an excel chart from scratch in PowerPoint (I am not sure why you would ever do that), and I can't figure out the syntax to insert an existing chart from excel.

Current code:

from pptx import Presentation

xlApp = win32.Dispatch('Excel.Application')
wb = xlApp.Workbooks.Open(outputPath+'Chart Pack.xlsb')
    
prs = Presentation(template)
slide = prs.slides[3]
    
for shape in slide.placeholders:
    print('%d %s' % (shape.placeholder_format.idx, shape.name))

placeholder = prs.slides[3].placeholders[1]
placeholder.name
placeholder.placeholder_format.type
placeholder.insert_chart(wb.sheets('Charts').ChartObjects('Figure1').Copy)

Requirements:

I would like to paste the excel chart as an excel object (rather than as a picture) as my colleague likes to be able to click on data series to get the underlying values etc.
I would like to paste the chart inside a placeholder (rather than on top of it).
I don't want to generate a new chart in PowerPoint as most examples do. A colleague has prepared an excel dashboard and formatted a number of charts as desired, and doing it all from scratch would be laborious.
I don't want to do this in VBA. I would like to do it in python as it is part of a broader program.
I don't want to use something like plotnine, seaborn, matplotlib etc. As per 1, my colleague likes excel objects that he can click on to show underlying values. This can't be done in these programs.

Python should be able to do this. Any ideas?

ANSWER

Answered 2022-Feb-12 at 02:55

You'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)

import win32com.client as win32

xlApp = win32.Dispatch('Excel.Application')
wb = xlApp.Workbooks.Open(outputPath+'Chart Pack.xlsb')

pptApp = win32.Dispatch('PowerPoint.Application')
ppt = pptApp.Presentations.Open(template)

slide_num = 3
LEFT_PLACEHOLDER = 3
RIGHT_PLACEHOLDER = 2

# Figure1
window.View.GotoSlide(slide_num)
wb.sheets('Charts').ChartObjects('Figure1').Copy()
ppt.Slides.Item(slide_num).Shapes.Paste().Select()
window.Selection.Cut()
ppt.Slides.Item(slide_num).Shapes(LEFT_PLACEHOLDER).Select()
window.View.Paste()

# Figure2
window.View.GotoSlide(slide_num)
wb.sheets('Charts').ChartObjects('Figure2').Copy()
ppt.Slides.Item(slide_num).Shapes.Paste().Select()
window.Selection.Cut()
ppt.Slides.Item(slide_num).Shapes(RIGHT_PLACEHOLDER).Select()
window.View.Paste()

EDIT: I was able to paste the chart directly into the placeholder after fiddling around a bit trying to implement the solution in this answer. It a bit hacky, but it works.

Source https://stackoverflow.com/questions/71031183

QUESTION

How to install local package with conda

Asked 2022-Feb-05 at 04:16

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.yaml so I could use conda-build to build jive locally
build jive with conda build .
I looked at the tarball that was produced and it does indeed contain the jive source 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:

package:
  name: jive
  version: "0.2.1"

source:
  path: .

build:
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  host:
     - python>=3.5
     - pip
     - setuptools
  run:
     - python>=3.5
     - numpy
     - pandas
     - scipy
     - seaborn
     - matplotlib
     - scikit-learn
     - statsmodels
     - joblib
     - bokeh

test:
  imports: jive

And here is the output of conda build .

No numpy version specified in conda_build_config.yaml.  Falling back to default numpy value of 1.16
WARNING:conda_build.metadata:No numpy version specified in conda_build_config.yaml.  Falling back to default numpy value of 1.16
Adding in variants from internal_defaults
INFO:conda_build.variants:Adding in variants from internal_defaults
Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /Users/thomaskeefe/.conda/conda_build_config.yaml
Attempting to finalize metadata for jive
INFO:conda_build.metadata:Attempting to finalize metadata for jive
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
BUILD START: ['jive-0.2.1-py310_0.tar.bz2']
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla


The following NEW packages will be INSTALLED:

    bzip2:           1.0.8-h1de35cc_0
    ca-certificates: 2021.10.26-hecd8cb5_2
    certifi:         2021.5.30-py310hecd8cb5_0
    libcxx:          12.0.0-h2f01273_0
    libffi:          3.3-hb1e8313_2
    ncurses:         6.3-hca72f7f_2
    openssl:         1.1.1m-hca72f7f_0
    pip:             21.2.4-py310hecd8cb5_0
    python:          3.10.0-hdfd78df_3
    readline:        8.1.2-hca72f7f_1
    setuptools:      58.0.4-py310hecd8cb5_0
    sqlite:          3.37.0-h707629a_0
    tk:              8.6.11-h7bc2e8c_0
    tzdata:          2021e-hda174b7_0
    wheel:           0.37.1-pyhd3eb1b0_0
    xz:              5.2.5-h1de35cc_0
    zlib:            1.2.11-h4dc903c_4

Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Copying /Users/thomaskeefe/Documents/py_jive to /opt/miniconda3/conda-bld/jive_1642185595622/work/
source tree in: /opt/miniconda3/conda-bld/jive_1642185595622/work
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pla
export BUILD_PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_build_env
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/work
Processing $SRC_DIR
  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
Building wheels for collected packages: jive
  Building wheel for jive (setup.py): started
  Building wheel for jive (setup.py): finished with status 'done'
  Created wheel for jive: filename=jive-0.2.1-py3-none-any.whl size=46071 sha256=b312955cb2fd917bc4e684a575407b884190680f2dddad7fcb9ac25e5b290fc9
  Stored in directory: /private/tmp/pip-ephem-wheel-cache-rbpkt2an/wheels/15/68/82/4ed7cd246fbc4c72cf764b425a03230247589bd2394a7e457b
Successfully built jive
Installing collected packages: jive
Successfully installed jive-0.2.1

Resource usage statistics from building jive:
   Process count: 3
   CPU time: Sys=0:00:00.3, User=0:00:00.5
   Memory: 53.7M
   Disk usage: 50.4K
   Time elapsed: 0:00:06.1


Packaging jive
INFO:conda_build.build:Packaging jive
INFO conda_build.build:build(2289): Packaging jive
Packaging jive-0.2.1-py310_0
INFO:conda_build.build:Packaging jive-0.2.1-py310_0
INFO conda_build.build:bundle_conda(1529): Packaging jive-0.2.1-py310_0
compiling .pyc files...
number of files: 70
Fixing permissions
INFO :: Time taken to mark (prefix)
        0 replacements in 0 files was 0.06 seconds
TEST START: /opt/miniconda3/conda-bld/osx-64/jive-0.2.1-py310_0.tar.bz2
Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO:conda_build.variants:Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
INFO conda_build.variants:_combine_spec_dictionaries(234): Adding in variants from /var/folders/dd/t85p2jdn3sd11bsdnl7th6p00000gn/T/tmp4o3im7d1/info/recipe/conda_build_config.yaml
Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO:conda_build.utils:Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
INFO conda_build.utils:shutil_move_more_retrying(2091): Renaming work directory '/opt/miniconda3/conda-bld/jive_1642185595622/work' to '/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64'
shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO:conda_build.utils:shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
INFO conda_build.utils:shutil_move_more_retrying(2098): shutil.move(work)=/opt/miniconda3/conda-bld/jive_1642185595622/work, dest=/opt/miniconda3/conda-bld/jive_1642185595622/work_moved_jive-0.2.1-py310_0_osx-64)
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done

## Package Plan ##

  environment location: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol


The following NEW packages will be INSTALLED:

    blas:              1.0-mkl
    bokeh:             2.4.2-py39hecd8cb5_0
    bottleneck:        1.3.2-py39he3068b8_1
    brotli:            1.0.9-hb1e8313_2
    ca-certificates:   2021.10.26-hecd8cb5_2
    certifi:           2021.10.8-py39hecd8cb5_2
    cycler:            0.11.0-pyhd3eb1b0_0
    fonttools:         4.25.0-pyhd3eb1b0_0
    freetype:          2.11.0-hd8bbffd_0
    giflib:            5.2.1-haf1e3a3_0
    intel-openmp:      2021.4.0-hecd8cb5_3538
    jinja2:            3.0.2-pyhd3eb1b0_0
    jive:              0.2.1-py310_0            local
    joblib:            1.1.0-pyhd3eb1b0_0
    jpeg:              9d-h9ed2024_0
    kiwisolver:        1.3.1-py39h23ab428_0
    lcms2:             2.12-hf1fd2bf_0
    libcxx:            12.0.0-h2f01273_0
    libffi:            3.3-hb1e8313_2
    libgfortran:       3.0.1-h93005f0_2
    libpng:            1.6.37-ha441bb4_0
    libtiff:           4.2.0-h87d7836_0
    libwebp:           1.2.0-hacca55c_0
    libwebp-base:      1.2.0-h9ed2024_0
    llvm-openmp:       12.0.0-h0dcd299_1
    lz4-c:             1.9.3-h23ab428_1
    markupsafe:        2.0.1-py39h9ed2024_0
    matplotlib:        3.5.0-py39hecd8cb5_0
    matplotlib-base:   3.5.0-py39h4f681db_0
    mkl:               2021.4.0-hecd8cb5_637
    mkl-service:       2.4.0-py39h9ed2024_0
    mkl_fft:           1.3.1-py39h4ab4a9b_0
    mkl_random:        1.2.2-py39hb2f4e1b_0
    munkres:           1.1.4-py_0
    ncurses:           6.3-hca72f7f_2
    numexpr:           2.8.1-py39h2e5f0a9_0
    numpy:             1.21.2-py39h4b4dc7a_0
    numpy-base:        1.21.2-py39he0bd621_0
    olefile:           0.46-pyhd3eb1b0_0
    openssl:           1.1.1m-hca72f7f_0
    packaging:         21.3-pyhd3eb1b0_0
    pandas:            1.3.5-py39h743cdd8_0
    patsy:             0.5.2-py39hecd8cb5_0
    pillow:            8.4.0-py39h98e4679_0
    pip:               21.2.4-py39hecd8cb5_0
    pyparsing:         3.0.4-pyhd3eb1b0_0
    python:            3.9.7-h88f2d9e_1
    python-dateutil:   2.8.2-pyhd3eb1b0_0
    pytz:              2021.3-pyhd3eb1b0_0
    pyyaml:            6.0-py39hca72f7f_1
    readline:          8.1.2-hca72f7f_1
    scikit-learn:      1.0.2-py39hae1ba45_0
    scipy:             1.7.3-py39h8c7af03_0
    seaborn:           0.11.2-pyhd3eb1b0_0
    setuptools:        58.0.4-py39hecd8cb5_0
    six:               1.16.0-pyhd3eb1b0_0
    sqlite:            3.37.0-h707629a_0
    statsmodels:       0.13.0-py39hca72f7f_0
    threadpoolctl:     2.2.0-pyh0d69192_0
    tk:                8.6.11-h7bc2e8c_0
    tornado:           6.1-py39h9ed2024_0
    typing_extensions: 3.10.0.2-pyh06a4308_0
    tzdata:            2021e-hda174b7_0
    wheel:             0.37.1-pyhd3eb1b0_0
    xz:                5.2.5-h1de35cc_0
    yaml:              0.2.5-haf1e3a3_0
    zlib:              1.2.11-h4dc903c_4
    zstd:              1.4.9-h322a384_0

Preparing transaction: ...working... done
Verifying transaction: ...working...
ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::intel-openmp-2021.4.0-hecd8cb5_3538, defaults/osx-64::llvm-openmp-12.0.0-h0dcd299_1
  path: 'lib/libiomp5.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'bin/webpinfo'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'bin/webpmux'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/decode.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/encode.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/mux.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/mux_types.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'include/webp/types.h'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.7.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebp.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.3.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpdecoder.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.3.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.a'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/libwebpmux.dylib'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebp.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebpdecoder.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'lib/pkgconfig/libwebpmux.pc'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/cwebp.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/dwebp.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/webpinfo.1'


ClobberWarning: This transaction has incompatible packages due to a shared path.
  packages: defaults/osx-64::libwebp-base-1.2.0-h9ed2024_0, defaults/osx-64::libwebp-1.2.0-hacca55c_0
  path: 'share/man/man1/webpmux.1'



done
Executing transaction: ...working...
ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/llvm-openmp-12.0.0-h0dcd299_1/lib/libiomp5.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libiomp5.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpinfo
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpinfo




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/bin/webpmux
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/bin/webpmux




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/decode.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/decode.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/encode.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/encode.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/mux_types.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/mux_types.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/include/webp/types.h
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/include/webp/types.h




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.7.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.7.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebp.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebp.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.3.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.3.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpdecoder.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpdecoder.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.3.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.3.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.a
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.a




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/lib/libwebpmux.dylib
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/libwebpmux.dylib




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/1018f8ab-87a7-4fa8-a41c-4c14cc77cfff
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebp.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/e3701fae-f2cd-44e9-9dc6-c71f499cd2c2
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpdecoder.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/.condatmp/0f4bcf50-01e5-404d-b1a4-8a87d45c22c5
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/lib/pkgconfig/libwebpmux.pc




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/cwebp.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/cwebp.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/dwebp.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/dwebp.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpinfo.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpinfo.1




ClobberWarning: Conda was asked to clobber an existing path.
  source path: /opt/miniconda3/pkgs/libwebp-1.2.0-hacca55c_0/share/man/man1/webpmux.1
  target path: /opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol/share/man/man1/webpmux.1





    Installed package of scikit-learn can be accelerated using scikit-learn-intelex.
    More details are available here: https://intel.github.io/scikit-learn-intelex

    For example:

        $ conda install scikit-learn-intelex
        $ python -m sklearnex my_application.py



done
export PREFIX=/opt/miniconda3/conda-bld/jive_1642185595622/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehol
export SRC_DIR=/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp
Traceback (most recent call last):
  File "/opt/miniconda3/conda-bld/jive_1642185595622/test_tmp/run_test.py", line 2, in 
    import jive
ModuleNotFoundError: No module named 'jive'
import: 'jive'
Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING:conda_build.build:Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
WARNING conda_build.build:tests_failed(2970): Tests failed for jive-0.2.1-py310_0.tar.bz2 - moving package to /opt/miniconda3/conda-bld/broken
TESTS FAILED: jive-0.2.1-py310_0.tar.bz2

EDIT: I added a test: section to the meta.yaml as merv suggested.

ANSWER

Answered 2022-Feb-05 at 04:16

The 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

package:
  name: jive
  version: "0.2.1"

source:
  path: .

build:
  script: python -m pip install --no-deps --ignore-installed .

requirements:
  host:
     - python
     - pip
     - setuptools
  run:
     - python
     - numpy
     - pandas
     - scipy
     - seaborn
     - matplotlib
     - scikit-learn
     - statsmodels
     - joblib
     - bokeh

If you want to use it in a Python 3.9 environment, then use conda build --python 3.9 ..

Source https://stackoverflow.com/questions/70705250

QUESTION

How to add median and IQR to seaborn violinplot

Asked 2022-Jan-14 at 18:59

I want to add the median and IQ values to the violin plot. However, I didn't find its argument.

import seaborn as sns
sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.violinplot(x=tips["total_bill"])
ax = sns.violinplot(x="day", y="total_bill", hue="sex",
                    data=tips, palette="Set2", split=True,
                    scale="count", inner="quartile")
plt.show()

For instance, I want such a notation for all of the graphs per gender:

I am fine to do it with other packages if it is available for them. However, I want to have violin plots rather than boxplots.

ANSWER

Answered 2022-Jan-14 at 18:59

If 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.

import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize':(20,14)})
sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.violinplot(x="day", y="total_bill", hue="sex",
                    data=tips, palette="Set2", split=True,
                    scale="count", inner="quartile")

for l in ax.lines:
    ax.text(l.get_data()[0][l.get_data()[0].nonzero()][0], l.get_data()[1][0], f'{l.get_data()[1][0]:.0f}',size='large')

for l in ax.lines:
    print(l.get_data())

Output

(array([-0.33143126,  0.        ]), array([13.6975, 13.6975]))
(array([-0.37082654,  0.        ]), array([16.975, 16.975]))
(array([-0.25003508,  0.        ]), array([22.36, 22.36]))
(array([0.        , 0.38725949]), array([12.1625, 12.1625]))
(array([0.        , 0.39493527]), array([13.785, 13.785]))
(array([0.        , 0.25722566]), array([18.675, 18.675]))
(array([0.61440819, 1.        ]), array([12.235, 12.235]))
(array([0.62200517, 1.        ]), array([17.215, 17.215]))
(array([0.71996702, 1.        ]), array([26.0825, 26.0825]))
(array([1.        , 1.27676537]), array([11.35, 11.35]))
(array([1.        , 1.35376254]), array([15.38, 15.38]))
(array([1.        , 1.33610081]), array([16.27, 16.27]))
(array([1.63945286, 2.        ]), array([13.905, 13.905]))
(array([1.60991669, 2.        ]), array([18.24, 18.24]))
(array([1.75431475, 2.        ]), array([24.165, 24.165]))
(array([2.        , 2.17418026]), array([14.05, 14.05]))
(array([2.        , 2.18356376]), array([18.36, 18.36]))
(array([2.        , 2.12950778]), array([25.5625, 25.5625]))
(array([2.62638135, 3.        ]), array([15.135, 15.135]))
(array([2.61919886, 3.        ]), array([20.725, 20.725]))
(array([2.73148987, 3.        ]), array([26.55, 26.55]))
(array([3.        , 3.12054879]), array([15.175, 15.175]))
(array([3.        , 3.12176923]), array([17.41, 17.41]))
(array([3.        , 3.07327664]), array([24.8975, 24.8975]))

Source https://stackoverflow.com/questions/70714905

QUESTION

Can't deploy streamlit app on share.streamlit.io

Asked 2021-Dec-25 at 14:42

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:

ERROR: Could not find a version that satisfies the requirement pywin32==303 (from versions: none)

ERROR: No matching distribution found for pywin32==303

This is the requirements.txt file for my model:

altair==4.1.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
astor==0.8.1
attrs==21.2.0
backcall==0.2.0
base58==2.1.1
bleach==4.1.0
blinker==1.4
cachetools==5.0.0
certifi==2021.10.8
cffi==1.15.0
charset-normalizer==2.0.9
click==7.1.2
colorama==0.4.4
cycler==0.11.0
debugpy==1.5.1
decorator==5.1.0
defusedxml==0.7.1
entrypoints==0.3
fonttools==4.28.5
gitdb==4.0.9
GitPython==3.1.24
idna==3.3
ipykernel==6.6.0
ipython==7.30.1
ipython-genutils==0.2.0
ipywidgets==7.6.5
jedi==0.18.1
Jinja2==3.0.3
joblib==1.1.0
jsonschema==4.3.2
jupyter-client==7.1.0
jupyter-core==4.9.1
jupyterlab-pygments==0.1.2
jupyterlab-widgets==1.0.2
kiwisolver==1.3.2
MarkupSafe==2.0.1
matplotlib==3.5.1
matplotlib-inline==0.1.3
mistune==0.8.4
nbclient==0.5.9
nbconvert==6.3.0
nbformat==5.1.3
nest-asyncio==1.5.4
notebook==6.4.6
numpy==1.21.5
packaging==21.3
pandas==1.3.5
pandocfilters==1.5.0
parso==0.8.3
pickleshare==0.7.5
Pillow==8.4.0
prometheus-client==0.12.0
prompt-toolkit==3.0.24
protobuf==3.19.1
pyarrow==6.0.1
pycparser==2.21
pydeck==0.7.1
Pygments==2.10.0
Pympler==1.0.1
pyparsing==3.0.6
pyrsistent==0.18.0
python-dateutil==2.8.2
pytz==2021.3
pytz-deprecation-shim==0.1.0.post0
pywin32==303
pywinpty==1.1.6
pyzmq==22.3.0
requests==2.26.0
scikit-learn==1.0.1
scipy==1.7.3
seaborn==0.11.2
Send2Trash==1.8.0
six==1.16.0
smmap==5.0.0
streamlit==1.3.0
terminado==0.12.1
testpath==0.5.0
threadpoolctl==3.0.0
toml==0.10.2
toolz==0.11.2
tornado==6.1
traitlets==5.1.1
typing_extensions==4.0.1
tzdata==2021.5
tzlocal==4.1
urllib3==1.26.7
validators==0.18.2
watchdog==2.1.6
wcwidth==0.2.5
webencodings==0.5.1
widgetsnbextension==3.5.2
wincertstore==0.2

What should I do to resolve this error?

ANSWER

Answered 2021-Dec-25 at 14:42

Streamlit 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.

Source https://stackoverflow.com/questions/70480314

QUESTION

Pandas agg define metric based on data type

Asked 2021-Dec-16 at 12:34

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:

import seaborn as sns
iris = sns.load_dataset('iris')

Desired code:

iris.agg({"object":"first", "float":"mean"})

ANSWER

Answered 2021-Dec-16 at 12:25

def 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)

Source https://stackoverflow.com/questions/70364384

QUESTION

Heatmap error :'NoneType' object is not callable when using with dataframe

Asked 2021-Dec-05 at 09:37

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:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax=sns.heatmap(flights)
plt.show()

ANSWER

Answered 2021-Dec-05 at 09:37

Use Python 3.9 (or 3.8, 3.7, 3.6) as it seems like both pandas and plt are not quite ready to be used with Python 3.10:

Source https://stackoverflow.com/questions/70232617

QUESTION

Adding columns & values per group occurrence in pandas after filtering

Asked 2021-Dec-03 at 09:52

I have a df

import seaborn as sns
import numpy as np
import pandas as pd

df = sns.load_dataset('titanic')

I am trying to add 8 columns from 1-8 which is the age of the passenger and to count how many passengers fit in to each of those categories 1-8

I tried:

df_temp = df.groupby('sex').agg({'fare':np.mean})

Which returns:

        fare
sex 
female  44.48
male    25.52

Then I have age_buckets = range(1,9) which are the age categories I am interested in.

The goal is to have this kind of structure:

sex            fare                 age_1       age_2       age_3         ...     age_8
male           25                   0           10          3                     x  
female         44                   1           2           2                     y

Which would mean that there were 0 age_1 males and 1 girl that was 1 year old and so on.

So I tried the following:

df_age = df[df.age.isin(age_buckets)].groupby(['sex','age']) \
                                  .count() \
                                  .survived \
                                  .reset_index()

Which returns the data I want but not in the format I can add to my primary df_temp as a column per age.

So at this point I am not sure if this is the right way to achieve the desired result I am trying to create and don't know how to work forward.

ANSWER

Answered 2021-Dec-03 at 09:52

Use cut with crosstab and add DataFrame.add_prefix:

df = (df_temp.join(pd.crosstab(df['sex'],pd.cut(df['age'], 
                                                bins=range(0,9),
                                                labels=range(1,9)))
                     .add_prefix('age_')))
print (df)
             fare  age_1  age_2  age_3  age_4  age_5  age_6  age_7  age_8
sex                                                                      
female  44.479818      4      6      2      5      4      2      1      2
male    25.523893     10      4      4      5      0      1      2      2

Source https://stackoverflow.com/questions/70212172

QUESTION

Matplotlib scatter plot marker type from dictionary

Asked 2021-Dec-02 at 18:42

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:

x = [4, 8, 1, 0, 2]
y = [0.1, 1, 0.4, 0.8, 0.9]
name = ["A", "A", "B", "A", "B"]
df = pd.DataFrame(data=zip(x, y, name), columns=["x", "y", "name"])

colors = {"A": "red", "B": "blue"}

fig, ax = plt.subplots(1, 1)
ax.scatter(
    x=df["x"],
    y=df["y"],
    facecolors="none",
    edgecolors=df["name"].map(colors),
)

But the following throws an error TypeError: 'Series' objects are mutable, thus they cannot be hashed:

markers = {"A": "v", "B": "D"}

fig, ax = plt.subplots(1, 1)
ax.scatter(
    x=df["x"],
    y=df["y"],
    facecolors="none",
    edgecolors=df["name"].map(colors),
    marker=df['name'].map(markers),
)

ANSWER

Answered 2021-Dec-02 at 18:42

Based 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.)

x = [4, 8, 1, 0, 2]
y = [0.1, 1, 0.4, 0.8, 0.9]
name = ["A", "A", "B", "A", "B"]
df = pd.DataFrame(data=zip(x, y, name), columns=["x", "y", "name"])

colors = {"A": "red", "B": "blue"}
markers = {"A": "v", "B": "D"}

fig, ax = plt.subplots(1, 1)

for name, group in df.groupby("name"):
    group = group.copy()
    m = markers.get(name)

    ax.scatter(
        x=group["x"],
        y=group["y"],
        facecolors="none",
        edgecolors=group["name"].map(colors),
        marker=m,
        label=name,
    )
    ax.legend(loc="lower right")

Source https://stackoverflow.com/questions/70191327

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Vulnerabilities

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Install seaborn

You can install using 'pip install seaborn' or download it from GitHub, PyPI.
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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