---
jupytext:
  formats: md:myst
  text_representation:
    format_name: myst
kernelspec:
  display_name: Python 3
  name: python3
---

```{code-cell} ipython3
---
tags: [remove-cell]
---
# Notebook setup
import os
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('../../site.mplstyle')
%matplotlib inline
from numpy_survey_results.utils import flatten, gluval
# Location of generated content
os.makedirs('_generated', exist_ok=True)
# For variable integration
from myst_nb import glue
```

# Usage

```{code-cell} ipython3
---
tags: [hide-input]
---
fname = "data/2020/numpy_survey_results.tsv"
column_names = [
    'using_random', 'bug', 'bug_resolution', 'bug_resolution_other',
    'unsolvable', 'unsolvable_resolution', 'unsolvable_resolution_other',
    'deprecation', 'deprecation_other'
]
featdep_dtype = np.dtype({
    "names": column_names,
    "formats": ['U1024'] * len(column_names),
})

data = np.loadtxt(
    fname, delimiter='\t', skiprows=3, dtype=featdep_dtype,
    usecols=range(87, 96), comments=None
)
```

This section comprises various questions to try to gain insight on things
like new feature adoption, issue resolution, and the length of deprecation
cycles.

## New `numpy.random` Adoption

A [new API for random number generation][nprandom] was added to `numpy.random`
in version 1.17.
We asked survey paricipants whether they were using the new random API.
Of the {glue}`demographics.md::num_respondents` survey participants,
{glue:text}`num_random_users` shared whether they were using the new `random`
API.

[nprandom]: https://numpy.org/doc/stable/reference/random/index.html

```{code-cell} ipython3
---
tags: [hide-input]
---
rand = data['using_random'][data['using_random'] != '']
labels, cnts = np.unique(rand, return_counts=True)

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(cnts, labels=labels, autopct='%1.1f%%')
fig.tight_layout()

glue(
    'num_random_users',
    gluval(rand.shape[0], data.shape[0]),
    display=False
)
```

(sec:handling_issues)=

## Handling Issues

We wanted to get a sense of how often users experience issues with NumPy, so
we asked the following question:

  > In the last year, have you experienced problems in code you’ve written
  > stemming from a problem in NumPy?

Of the {glue}`demographics.md::num_respondents` survey participants,
{glue:text}`bug_reporters` responded to this question.

```{code-cell} ipython3
---
tags: [hide-input]
---
bug = data['bug'][data['bug'] != '']
labels, cnts = np.unique(bug, return_counts=True)

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(cnts, labels=labels, autopct='%1.1f%%', labeldistance=None)
ax.legend()
fig.tight_layout()

glue(
    'bug_reporters',
    gluval(bug.shape[0], data.shape[0]),
    display=False,
)
```

We asked those who reported experience issues what action(s) they took to
resolve the issue.

```{code-cell} ipython3
---
tags: [hide-input]
---
bug_resolution = data['bug_resolution'][data['bug_resolution'] != '']
labels, cnts = np.unique(flatten(bug_resolution), return_counts=True)
I = np.argsort(cnts)
labels, cnts = labels[I], cnts[I]

fig, ax = plt.subplots(figsize=(12, 8))
ax.barh(
    np.arange(len(labels)),
    100 * cnts / bug_resolution.shape[0], 
    tick_label=labels,
)
ax.set_xlabel('Percentage of Respondents')
fig.tight_layout()
```

## Data Analysis with NumPy

Similar to the {ref}`the previous question <sec:handling_issues>`, we tried to
get a sense of how well NumPy meets users' data analysis needs.
We asked the following question:

  > In the last year, have you encountered a problem involving numerical data
  > that you were unable to solve using NumPy?

Of the {glue}`demographics.md::num_respondents` survey participants, 
{glue:text}`num_solvers` responded to the above question, with 
{glue:text}`num_unsolved` reporting that they've had a problem that they 
initially expected to be able to solve using NumPy, but were unable to do so.

```{code-cell} ipython3
---
tags: [hide-input]
---
unsolvable = data['unsolvable'][data['unsolvable'] != '']
labels, cnts = np.unique(unsolvable, return_counts=True)
num_yes = np.sum(unsolvable == 'Yes')

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(cnts, labels=labels, autopct='%1.1f%%')
fig.tight_layout()

glue(
    'num_solvers',
    gluval(unsolvable.shape[0], data.shape[0]),
    display=False,
)
glue(
    'num_unsolved',
    gluval(num_yes, unsolvable.shape[0]),
    display=False
)
```

We asked those that responded "Yes" to the previous question what action(s)
they took to resolve the issue.

```{code-cell} ipython3
---
tags: [hide-input]
---
resolution = data['unsolvable_resolution'][data['unsolvable'] == 'Yes']
resolution = resolution[resolution != '']
labels, cnts = np.unique(flatten(resolution), return_counts=True)
I = np.argsort(cnts)
labels, cnts = labels[I], cnts[I]

fig, ax = plt.subplots(figsize=(12, 8))
ax.barh(
    np.arange(len(labels)),
    100 * cnts / resolution.shape[0], 
    tick_label=labels,
)
ax.set_xlabel('Percentage of Respondents')
fig.tight_layout()
```

## Deprecation Timeframe

We asked survey participants to share their opinion on the NumPy
deprecation cycle, specifically:

  > What do you consider as a good deprecation time frame?

Of the {glue}`demographics.md::num_respondents` survey participants,
{glue:text}`dep_opinions` responded to this question.

```{code-cell} ipython3
---
tags: [hide-input]
---
depcycle = data['deprecation'][data['deprecation'] != '']
labels, cnts = np.unique(depcycle, return_counts=True)

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(cnts, labels=labels, autopct='%1.1f%%')
fig.tight_layout()

glue(
    'dep_opinions',
    gluval(depcycle.shape[0], data.shape[0]),
    display=False
)
```