Mplot

What's Changed

• Mplot by @LSYS in #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:

pip install forestplot

Quickstart:

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: v0.3.3...v0.4.0

v0.3.3

What's Changed

• Add axis object as argument to forest plot by @juancq in #73
• Warn about duplicated varlabel (closes #76, closes #81).
• Add test that above warning works.
• Add known issues about duplicated varlabel (closes #76, closes #81) and PyCharm (closes #80).

New Contributors

• @juancq made their first contribution in #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:

pip install forestplot

Quickstart:

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: v0.3.2...v0.3.3

v0.3.2

What's Changed

• Patch to fix bug for newer matplotlib versions (by @LSYS in #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:

pip install forestplot

Quickstart:

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: v0.2.2...v0.3.2

v0.3.1

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:

pip install forestplot

Quickstart:

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: v0.2.2...v0.3.1

v0.3.0

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:

pip install forestplot

Quickstart:

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:

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: v0.2.2...v0.3.0

v0.2.2

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:

pip install forestplot

Quickstart:

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: v0.2.0...v0.2.2

v0.2.1

What's Changed

• Patch by @LSYS in #42

• Remove excessive vertical whitespace

• Add matplotlib-inline as requirement

• Add note to save using bbox_inches="tight"

• Add versioning

• Add recipe to run readme-examples.ipynb notebook

Full Changelog: v0.2.0...v0.2.1

v0.2.0

What's Changed

• Create workflow to check links in readme.md by @LSYS in #14
• Patch by @LSYS in #16
• Update docs & fix group subheadings order by @LSYS in #20
• Add logscale option, make no string normalization the default by @LSYS in #34
• add wheel to build, include a requirements_dev.txt and document by @shapiromatron in #35
• Fix typo in readme by @shapiromatron in #31
• Better backend for Confidence Intervals (closes #29)
• Plotting of estimates on a log-scale (closes #28)
• Maintain label character formatting (making no string normalisation the default, closes #27)

tldr

• logscale is now an option
• default is now not to normalize strings

New Contributors

• @shapiromatron made their first contribution in #35

Full Changelog: v0.0.4...v0.2.0

Release of Forestplot v0.1.0

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

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

To install via PyPI:

pip install forestplot

Quickstart:

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
              )

More customizations are available, for example:

Alpha

Bumping to v0.0.4 (set up CI/workflows)