%global _empty_manifest_terminate_build 0
Name:		python-chart
Version:	0.2.3
Release:	1
Summary:	chart
License:	MIT
URL:		https://github.com/maxhumber/chart
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b3/e0/b10edf6b4ed5d4bc26b8d9b63d769b85efac89f186a733c73147561f1dd3/chart-0.2.3.tar.gz
BuildArch:	noarch


%description
<h3 align="center">
  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
</h3>
<p align="center">
  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
</p>

A zero-dependency python package that prints basic charts to a Jupyter output

Charts supported:

- Bar graphs
- Scatter plots
- Histograms
- 🍑📊👏

#### Examples

Bar graphs can be drawn quickly with the `bar` function:

```python
from chart import bar

x = [500, 200, 900, 400]
y = ['marc', 'mummify', 'chart', 'sausagelink']

bar(x, y)
```

```
       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
    mummify: ▇▇▇▇▇▇▇                       
      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
```

And the `bar` function can accept columns from a `pd.DataFrame`:

```python
from chart import bar
import pandas as pd

df = pd.DataFrame({
    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
    'listens': [8_456_831, 18_185_245, 2_556_448]
})
bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
```

```
Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
 The Knocks: 🔊🔊🔊                                
```

Histograms are just as easy:

```python
from chart import histogram

x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]

histogram(x)
```

```
▇        
▇        
▇        
▇        
▇ ▇      
▇ ▇      
▇ ▇      
▇ ▇     ▇
▇ ▇     ▇
▇ ▇   ▇ ▇
```

And they can accept objects created by `scipy`:

```python
from chart import histogram
import scipy.stats as stats
import numpy as np

np.random.seed(14)
n = stats.norm(loc=0, scale=10)

histogram(n.rvs(100), bins=14, height=7, mark='🍑')
```

```
            🍑              
            🍑   🍑          
            🍑 🍑 🍑          
            🍑 🍑 🍑          
        🍑   🍑 🍑 🍑          
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
```

Scatter plots can be drawn with a simple `scatter` call:

```python
from chart import scatter

x = range(0, 20)
y = range(0, 20)

scatter(x, y)
```

```python
                                       •
                                   • •  
                                 •      
                             • •        
                         • •            
                       •                
                  •  •                  
                •                       
            • •                         
        • •                             
      •                                 
  • •                                   
•                                       
```

And at this point you gotta know it works with any `np.array`:

```python
from chart import scatter
import numpy as np

np.random.seed(1)
N = 100
x = np.random.normal(100, 50, size=N)
y = x * -2 + 25 + np.random.normal(0, 25, size=N)

scatter(x, y, width=20, height=9, mark='^')
```

```
^^                  
 ^                  
    ^^^             
    ^^^^^^^         
       ^^^^^^       
        ^^^^^^^     
            ^^^^    
             ^^^^^ ^
                ^^ ^
```

In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.

#### Preprocessors

In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.

I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:

```python
from chart.preprocessing import NumberBinarizer

nb = NumberBinarizer(bins=4)
x = range(10)
nb.fit(x)
nb.transform(x)
```

```
[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
```

```python
from chart.preprocessing import RangeScaler

rs = RangeScaler(out_range=(0, 10), round=False)
x = range(50, 59)
rs.fit_transform(x)
```

```
[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
```

#### Installation

```python
pip install chart
```

#### Contribute

For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)

#### Inspiration

I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))

%package -n python3-chart
Summary:	chart
Provides:	python-chart
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-chart
<h3 align="center">
  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
</h3>
<p align="center">
  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
</p>

A zero-dependency python package that prints basic charts to a Jupyter output

Charts supported:

- Bar graphs
- Scatter plots
- Histograms
- 🍑📊👏

#### Examples

Bar graphs can be drawn quickly with the `bar` function:

```python
from chart import bar

x = [500, 200, 900, 400]
y = ['marc', 'mummify', 'chart', 'sausagelink']

bar(x, y)
```

```
       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
    mummify: ▇▇▇▇▇▇▇                       
      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
```

And the `bar` function can accept columns from a `pd.DataFrame`:

```python
from chart import bar
import pandas as pd

df = pd.DataFrame({
    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
    'listens': [8_456_831, 18_185_245, 2_556_448]
})
bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
```

```
Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
 The Knocks: 🔊🔊🔊                                
```

Histograms are just as easy:

```python
from chart import histogram

x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]

histogram(x)
```

```
▇        
▇        
▇        
▇        
▇ ▇      
▇ ▇      
▇ ▇      
▇ ▇     ▇
▇ ▇     ▇
▇ ▇   ▇ ▇
```

And they can accept objects created by `scipy`:

```python
from chart import histogram
import scipy.stats as stats
import numpy as np

np.random.seed(14)
n = stats.norm(loc=0, scale=10)

histogram(n.rvs(100), bins=14, height=7, mark='🍑')
```

```
            🍑              
            🍑   🍑          
            🍑 🍑 🍑          
            🍑 🍑 🍑          
        🍑   🍑 🍑 🍑          
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
```

Scatter plots can be drawn with a simple `scatter` call:

```python
from chart import scatter

x = range(0, 20)
y = range(0, 20)

scatter(x, y)
```

```python
                                       •
                                   • •  
                                 •      
                             • •        
                         • •            
                       •                
                  •  •                  
                •                       
            • •                         
        • •                             
      •                                 
  • •                                   
•                                       
```

And at this point you gotta know it works with any `np.array`:

```python
from chart import scatter
import numpy as np

np.random.seed(1)
N = 100
x = np.random.normal(100, 50, size=N)
y = x * -2 + 25 + np.random.normal(0, 25, size=N)

scatter(x, y, width=20, height=9, mark='^')
```

```
^^                  
 ^                  
    ^^^             
    ^^^^^^^         
       ^^^^^^       
        ^^^^^^^     
            ^^^^    
             ^^^^^ ^
                ^^ ^
```

In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.

#### Preprocessors

In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.

I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:

```python
from chart.preprocessing import NumberBinarizer

nb = NumberBinarizer(bins=4)
x = range(10)
nb.fit(x)
nb.transform(x)
```

```
[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
```

```python
from chart.preprocessing import RangeScaler

rs = RangeScaler(out_range=(0, 10), round=False)
x = range(50, 59)
rs.fit_transform(x)
```

```
[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
```

#### Installation

```python
pip install chart
```

#### Contribute

For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)

#### Inspiration

I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))

%package help
Summary:	Development documents and examples for chart
Provides:	python3-chart-doc
%description help
<h3 align="center">
  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
</h3>
<p align="center">
  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
</p>

A zero-dependency python package that prints basic charts to a Jupyter output

Charts supported:

- Bar graphs
- Scatter plots
- Histograms
- 🍑📊👏

#### Examples

Bar graphs can be drawn quickly with the `bar` function:

```python
from chart import bar

x = [500, 200, 900, 400]
y = ['marc', 'mummify', 'chart', 'sausagelink']

bar(x, y)
```

```
       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
    mummify: ▇▇▇▇▇▇▇                       
      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
```

And the `bar` function can accept columns from a `pd.DataFrame`:

```python
from chart import bar
import pandas as pd

df = pd.DataFrame({
    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
    'listens': [8_456_831, 18_185_245, 2_556_448]
})
bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
```

```
Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
 The Knocks: 🔊🔊🔊                                
```

Histograms are just as easy:

```python
from chart import histogram

x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]

histogram(x)
```

```
▇        
▇        
▇        
▇        
▇ ▇      
▇ ▇      
▇ ▇      
▇ ▇     ▇
▇ ▇     ▇
▇ ▇   ▇ ▇
```

And they can accept objects created by `scipy`:

```python
from chart import histogram
import scipy.stats as stats
import numpy as np

np.random.seed(14)
n = stats.norm(loc=0, scale=10)

histogram(n.rvs(100), bins=14, height=7, mark='🍑')
```

```
            🍑              
            🍑   🍑          
            🍑 🍑 🍑          
            🍑 🍑 🍑          
        🍑   🍑 🍑 🍑          
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
```

Scatter plots can be drawn with a simple `scatter` call:

```python
from chart import scatter

x = range(0, 20)
y = range(0, 20)

scatter(x, y)
```

```python
                                       •
                                   • •  
                                 •      
                             • •        
                         • •            
                       •                
                  •  •                  
                •                       
            • •                         
        • •                             
      •                                 
  • •                                   
•                                       
```

And at this point you gotta know it works with any `np.array`:

```python
from chart import scatter
import numpy as np

np.random.seed(1)
N = 100
x = np.random.normal(100, 50, size=N)
y = x * -2 + 25 + np.random.normal(0, 25, size=N)

scatter(x, y, width=20, height=9, mark='^')
```

```
^^                  
 ^                  
    ^^^             
    ^^^^^^^         
       ^^^^^^       
        ^^^^^^^     
            ^^^^    
             ^^^^^ ^
                ^^ ^
```

In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.

#### Preprocessors

In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.

I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:

```python
from chart.preprocessing import NumberBinarizer

nb = NumberBinarizer(bins=4)
x = range(10)
nb.fit(x)
nb.transform(x)
```

```
[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
```

```python
from chart.preprocessing import RangeScaler

rs = RangeScaler(out_range=(0, 10), round=False)
x = range(50, 59)
rs.fit_transform(x)
```

```
[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
```

#### Installation

```python
pip install chart
```

#### Contribute

For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)

#### Inspiration

I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))

%prep
%autosetup -n chart-0.2.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-chart -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.3-1
- Package Spec generated