Forestplot

What's Changed
* Mplot by LSYS in https://github.com/LSYS/forestplot/pull/108

Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


**Quickstart:**
python
import forestplot as fp

df_mmodel = pd.read_csv("../examples/data/sleep-mmodel.csv").query(
"model=='all' | model=='young kids'"
)

fp.mforestplot(
dataframe=df_mmodel,
estimate="coef",
ll="ll",
hl="hl",
varlabel="label",
capitalize="capitalize",
model_col="model",
color_alt_rows=True,
groupvar="group",
table=True,
rightannote=["var", "group"],
right_annoteheaders=["Source", "Group"],
xlabel="Coefficient (95% CI)",
modellabels=["Have young kids", "Full sample"],
xticks=[-1200, -600, 0, 600],
mcolor=["CC6677", "4477AA"],
Additional kwargs for customizations
**{
"markersize": 30,
override default vertical offset between models (0.0 to 1.0)
"offset": 0.35,
"xlinestyle": (0, (10, 5)), long dash for x-reference line
"xlinecolor": ".8", gray color for x-reference line
},
)


**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.3.3...v0.4.0

What's Changed
* Add axis object as argument to forest plot by juancq in https://github.com/LSYS/forestplot/pull/73
* Warn about duplicated `varlabel` (closes https://github.com/LSYS/forestplot/issues/76, closes https://github.com/LSYS/forestplot/issues/81).
* Add test that above warning works.
* Add known issues about duplicated `varlabel` (closes https://github.com/LSYS/forestplot/issues/76, closes https://github.com/LSYS/forestplot/issues/81) and PyCharm (closes https://github.com/LSYS/forestplot/issues/80).


New Contributors
* juancq made their first contribution in https://github.com/LSYS/forestplot/pull/73

Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


Quickstart:
python
import forestplot as fp
df = fp.load_data("sleep") companion example data

fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
ll="ll", hl="hl", columns containing conf. int. lower and higher limits
varlabel="label", column containing variable label
ylabel="Confidence interval", y-label title
xlabel="Pearson correlation" x-label title
)




**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.3.2...v0.3.3

What's Changed

* Patch to fix bug for newer matplotlib versions (by LSYS in https://github.com/LSYS/forestplot/pull/85).
* Thanks to maikia for flagging and Tian-hao for solution (82).
* No user-facing changes.

Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


Quickstart:
python
import forestplot as fp
df = fp.load_data("sleep") companion example data

fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
ll="ll", hl="hl", columns containing conf. int. lower and higher limits
varlabel="label", column containing variable label
ylabel="Confidence interval", y-label title
xlabel="Pearson correlation" x-label title
)




**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.2.2...v0.3.2

What's Changed
No user-facing changes.

Pandas `append` API in the backend is deprecated and so replaced by `concat`. This should accommodate newer versions of Pandas, like the recent v2.0 release.


Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


Quickstart:
python
import forestplot as fp
df = fp.load_data("sleep") companion example data

fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
ll="ll", hl="hl", columns containing conf. int. lower and higher limits
varlabel="label", column containing variable label
ylabel="Confidence interval", y-label title
xlabel="Pearson correlation" x-label title
)



**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.2.2...v0.3.1

What's Changed
Main user-facing change is that no drawing of CI (confidence intervals) is now possible.

* Allow no drawing of CI 58
* Update docs accordingly to reflect that `ll` and `hl` options are no longer required

Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


Quickstart:
python
import forestplot as fp
df = fp.load_data("sleep") companion example data

fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
ll="ll", hl="hl", columns containing conf. int. lower and higher limits
varlabel="label", column containing variable label
ylabel="Confidence interval", y-label title
xlabel="Pearson correlation" x-label title
)


No CI:

python
fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
varlabel="label", column containing variable label
)



See the README for more customizations.

**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.2.2...v0.3.0

What's Changed
* Fix spacing issue at top of plot (fixes 48, 47)
* Create notebook for some simple regression tests (closes 49)
* Tidy imports using isort (closes 50)
* Allowed thresholds and symbols for p-values to be passedthrough (fixes 51)
* Fix different heigh and fontsize for confidence interval and p-value labels (fixes 53)
* Update docs for RTD (closes 54)
* Freeze matplotlib-inline dependency in setup.py (closes 56)

Forestplot

`forestplot` is a Python package to make publication-ready but customizable coefficient plots.

* GitHub repo: https://github.com/LSYS/forestplot
* Docs: https://forestplot.readthedocs.io

To install via PyPI:
console
pip install forestplot


Quickstart:
python
import forestplot as fp
df = fp.load_data("sleep") companion example data

fp.forestplot(df, the dataframe with results data
estimate="r", col containing estimated effect size
ll="ll", hl="hl", columns containing conf. int. lower and higher limits
varlabel="label", column containing variable label
ylabel="Confidence interval", y-label title
xlabel="Pearson correlation" x-label title
)


See the README for more customizations.

**Full Changelog**: https://github.com/LSYS/forestplot/compare/v0.2.0...v0.2.2