# pandas-plots
   
## usage
install / update package
```bash
pip install pandas-plots -U
```
include in python
```python
from pandas_plots import tbl, pls, ven, hlp
```
## example
```python
# load sample dataset from seaborn
import seaborn as sb
df = sb.load_dataset('taxis')
```
```python
_df = df[["passengers", "distance", "fare"]][:5]
tbl.show_num_df(
_df,
total_axis="xy",
total_mode="mean",
data_bar_axis="xy",
pct_axis="xy",
precision=0,
kpi_mode="max_min_x",
kpi_rag_list=(1,7),
)
```
## why use pandas-plots
`pandas-plots` is a package to help you examine and visualize data that are organized in a pandas DataFrame. It provides a high level api to pandas / plotly with some selected functions and predefined options:
- `tbl` utilities for table descriptions
- 🌟`show_num_df()` displays a table as styled version with additional information
- `describe_df()` an alternative version of pandas `describe()` function
- `descr_db()` a very short descr for a `duckdb` relation
- `pivot_df()` gets a pivot table of a 3 column dataframe (or 2 columns if no weights are given)
- `print_summary()` shows statistics for a pandas DataFrame or Series
<br>
- `pls` for plotly visualizations
- `plot_box()` auto annotated boxplot w/ violin option
- `plot_boxes()` multiple boxplots _(annotation is experimental)_
- `plot_stacked_bars()` shortcut to stacked bars 😄
- `plots_bars()` a standardized bar plot for a **categorical** column
- features confidence intervals via `use_ci` option
- `plot_histogram()` histogram for one or more **numerical** columns
- `plot_joints()` a joint plot for **exactly two numerical** columns
- `plot_quadrants()` quickly shows a 2x2 heatmap
- `plot_facet_stacked_bars()` shows stacked bars for a facet value as subplots
<br>
- `ven` offers functions for _venn diagrams_
- `show_venn2()` displays a venn diagram for 2 sets
- `show_venn3()` displays a venn diagram for 3 sets
<br>
- `hlp` contains some (variety) helper functions
- `to_series()` converts a dataframe to a series (`🚨 breaking change`)
- `mean_confidence_interval()` calculates mean and confidence interval for a series
- `wrap_text()` formats strings or lists to a given width to fit nicely on the screen
- `replace_delimiter_outside_quotes()` when manual import of csv files is needed: replaces delimiters only outside of quotes
- `create_barcode_from_url()` creates a barcode from a given URL
- `add_datetime_col()` adds a datetime columns to a dataframe (chainable)
- `show_package_version` prints version of a list of packages
- `get_os` helps to identify and ensure operating system at runtime
- `add_bitmask_label()` adds a column to the data that resolves a bitmask column into human-readable labels
- `find_cols()` finds all columns in a list of columns that contain any of the given stubs
- `add_measures_to_pyg_config()` adds measures to a pygwalker config file to avoid frequent manual update
<br>
> note: theme setting can be controlled through all functions by setting the environment variable `THEME` to either light or dark
## more examples
```python
pls.plot_box(df['fare'], height=400, violin=True)
```
```python
# quick and exhaustive description of any table
tbl.describe_df(df, 'taxis', top_n_uniques=5)
```
```python
# show bars with confidence intervals
_df = df[["payment", "fare"]]
pls.plot_bars(
_df,
dropna=False,
use_ci=True,
height=600,
width=800,
precision=1,
)
```
```python
# show venn diagram for 3 sets
from pandas_plots import ven
set_a = {'ford','ferrari','mercedes', 'bmw'}
set_b = {'opel','bmw','bentley','audi'}
set_c = {'ferrari','bmw','chrysler','renault','peugeot','fiat'}
_df, _details = ven.show_venn3(
title="taxis",
a_set=set_a,
a_label="cars1",
b_set=set_b,
b_label="cars2",
c_set=set_c,
c_label="cars3",
verbose=0,
size=8,
)
```
## tags
#pandas, #plotly, #visualizations, #statistics
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"description": "# pandas-plots\n\n   \n\n## usage\n\ninstall / update package\n\n```bash\npip install pandas-plots -U\n```\n\ninclude in python\n\n```python\nfrom pandas_plots import tbl, pls, ven, hlp\n```\n\n## example\n\n```python\n# load sample dataset from seaborn\nimport seaborn as sb\ndf = sb.load_dataset('taxis')\n```\n\n```python\n_df = df[[\"passengers\", \"distance\", \"fare\"]][:5]\ntbl.show_num_df(\n _df,\n total_axis=\"xy\",\n total_mode=\"mean\",\n data_bar_axis=\"xy\",\n pct_axis=\"xy\",\n precision=0,\n kpi_mode=\"max_min_x\",\n kpi_rag_list=(1,7),\n)\n```\n\n\n\n## why use pandas-plots\n\n`pandas-plots` is a package to help you examine and visualize data that are organized in a pandas DataFrame. It provides a high level api to pandas / plotly with some selected functions and predefined options:\n\n- `tbl` utilities for table descriptions\n - \ud83c\udf1f`show_num_df()` displays a table as styled version with additional information\n - `describe_df()` an alternative version of pandas `describe()` function\n - `descr_db()` a very short descr for a `duckdb` relation\n - `pivot_df()` gets a pivot table of a 3 column dataframe (or 2 columns if no weights are given)\n - `print_summary()` shows statistics for a pandas DataFrame or Series\n<br>\n\n- `pls` for plotly visualizations\n - `plot_box()` auto annotated boxplot w/ violin option\n - `plot_boxes()` multiple boxplots _(annotation is experimental)_\n - `plot_stacked_bars()` shortcut to stacked bars \ud83d\ude04\n - `plots_bars()` a standardized bar plot for a **categorical** column\n - features confidence intervals via `use_ci` option\n - `plot_histogram()` histogram for one or more **numerical** columns\n - `plot_joints()` a joint plot for **exactly two numerical** columns\n - `plot_quadrants()` quickly shows a 2x2 heatmap\n - `plot_facet_stacked_bars()` shows stacked bars for a facet value as subplots \n<br>\n\n- `ven` offers functions for _venn diagrams_\n - `show_venn2()` displays a venn diagram for 2 sets\n - `show_venn3()` displays a venn diagram for 3 sets\n<br>\n\n- `hlp` contains some (variety) helper functions\n - `to_series()` converts a dataframe to a series (`\ud83d\udea8 breaking change`)\n - `mean_confidence_interval()` calculates mean and confidence interval for a series\n - `wrap_text()` formats strings or lists to a given width to fit nicely on the screen\n - `replace_delimiter_outside_quotes()` when manual import of csv files is needed: replaces delimiters only outside of quotes\n - `create_barcode_from_url()` creates a barcode from a given URL\n - `add_datetime_col()` adds a datetime columns to a dataframe (chainable)\n - `show_package_version` prints version of a list of packages\n - `get_os` helps to identify and ensure operating system at runtime\n - `add_bitmask_label()` adds a column to the data that resolves a bitmask column into human-readable labels\n - `find_cols()` finds all columns in a list of columns that contain any of the given stubs\n - `add_measures_to_pyg_config()` adds measures to a pygwalker config file to avoid frequent manual update\n<br>\n\n> note: theme setting can be controlled through all functions by setting the environment variable `THEME` to either light or dark\n\n## more examples\n\n```python\npls.plot_box(df['fare'], height=400, violin=True)\n```\n\n\n\n```python\n# quick and exhaustive description of any table\ntbl.describe_df(df, 'taxis', top_n_uniques=5)\n```\n\n\n\n```python\n# show bars with confidence intervals\n_df = df[[\"payment\", \"fare\"]]\npls.plot_bars(\n _df,\n dropna=False,\n use_ci=True,\n height=600,\n width=800,\n precision=1,\n)\n```\n\n\n\n```python\n# show venn diagram for 3 sets\nfrom pandas_plots import ven\n\nset_a = {'ford','ferrari','mercedes', 'bmw'}\nset_b = {'opel','bmw','bentley','audi'}\nset_c = {'ferrari','bmw','chrysler','renault','peugeot','fiat'}\n_df, _details = ven.show_venn3(\n title=\"taxis\",\n a_set=set_a,\n a_label=\"cars1\",\n b_set=set_b,\n b_label=\"cars2\",\n c_set=set_c,\n c_label=\"cars3\",\n verbose=0,\n size=8,\n)\n```\n\n\n\n## tags\n\n#pandas, #plotly, #visualizations, #statistics\n",
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