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Lint, remove deprecated features (ResidentMario#137)
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@ResidentMario
ResidentMario committed Jul 3, 2021
1 parent 6ad031d commit 5e5607c
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Showing 2 changed files with 15 additions and 124 deletions.
2 changes: 1 addition & 1 deletion missingno/_version.py
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Original file line number Diff line number Diff line change
@@ -1 +1 @@
__version__ = '0.4.2'
__version__ = '0.4.3'
137 changes: 14 additions & 123 deletions missingno/missingno.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,14 +193,7 @@ def matrix(df,
# Remove tick mark (only works after plotting).
ax1.xaxis.set_ticks_position('none')

if inline:
warnings.warn(
"The 'inline' argument has been deprecated, and will be removed in a future version "
"of missingno."
)
plt.show()
else:
return ax0
return ax0


def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray', inline=False,
Expand Down Expand Up @@ -232,7 +225,7 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',
orientation = 'left'
else:
orientation = 'bottom'

if ax is None:
ax1 = plt.gca()
if figsize is None:
Expand All @@ -248,7 +241,7 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',
if orientation == 'bottom':
(nullity_counts / len(df)).plot.bar(**plot_args)
else:
(nullity_counts / len(df)).plot.barh(**plot_args)
(nullity_counts / len(df)).plot.barh(**plot_args)

axes = [ax1]

Expand All @@ -262,13 +255,14 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',
if not log:
ax1.set_ylim([0, 1])
ax2.set_yticks(ax1.get_yticks())
ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
else:
# For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
# appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
# is used, we have to make it match the `ax1` layout ourselves.
ax2.set_yscale('log')
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
ax2.set_yticklabels([int(n * len(df)) for n in ax1.get_yticks()], fontsize=fontsize)

# Create the third axis, which displays columnar totals above the rest of the plot.
ax3 = ax1.twiny()
Expand All @@ -287,7 +281,7 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',

# Bottom
ax2.set_xticks(ax1.get_xticks())
ax2.set_xticklabels([int(n*len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
ax2.set_xticklabels([int(n * len(df)) for n in ax1.get_xticks()], fontsize=fontsize)

# Right
ax2.set_yticks(ax1.get_yticks())
Expand All @@ -301,7 +295,7 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',

# Bottom
ax2.set_xticks(ax1.get_xticks())
ax2.set_xticklabels([int(n*len(df)) for n in ax1.get_xticks()], fontsize=fontsize)
ax2.set_xticklabels([int(n * len(df)) for n in ax1.get_xticks()], fontsize=fontsize)

# Right
ax2.set_yticks(ax1.get_yticks())
Expand All @@ -327,24 +321,17 @@ def bar(df, figsize=None, fontsize=16, labels=None, log=False, color='dimgray',
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

if inline:
warnings.warn(
"The 'inline' argument has been deprecated, and will be removed in a future version "
"of missingno."
)
plt.show()
else:
return ax1
return ax1


def heatmap(df, inline=False,
filter=None, n=0, p=0, sort=None,
figsize=(20, 12), fontsize=16, labels=True,
figsize=(20, 12), fontsize=16, labels=True,
cmap='RdBu', vmin=-1, vmax=1, cbar=True, ax=None
):
"""
Presents a `seaborn` heatmap visualization of nullity correlation in the given DataFrame.
Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.
:param df: The DataFrame whose completeness is being heatmapped.
Expand Down Expand Up @@ -376,7 +363,7 @@ def heatmap(df, inline=False,
ax0 = ax

# Remove completely filled or completely empty variables.
df = df.iloc[:,[i for i, n in enumerate(np.var(df.isnull(), axis='rows')) if n > 0]]
df = df.iloc[:, [i for i, n in enumerate(np.var(df.isnull(), axis='rows')) if n > 0]]

# Create and mask the correlation matrix. Construct the base heatmap.
corr_mat = df.isnull().corr()
Expand Down Expand Up @@ -415,14 +402,7 @@ def heatmap(df, inline=False,
else:
text.set_text(round(t, 1))

if inline:
warnings.warn(
"The 'inline' argument has been deprecated, and will be removed in a future version "
"of missingno."
)
plt.show()
else:
return ax0
return ax0


def dendrogram(df, method='average',
Expand All @@ -438,7 +418,7 @@ def dendrogram(df, method='average',
left unspecified the dendrogram will automatically swap to a horizontal display to fit the additional variables.
:param df: The DataFrame whose completeness is being dendrogrammed.
:param method: The distance measure being used for clustering. This is a parameter that is passed to
:param method: The distance measure being used for clustering. This is a parameter that is passed to
`scipy.hierarchy`.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
Expand Down Expand Up @@ -508,93 +488,4 @@ def dendrogram(df, method='average',
else:
ax0.tick_params(axis='x', labelsize=int(fontsize / 16 * 20))

if inline:
warnings.warn(
"The 'inline' argument has been deprecated, and will be removed in a future version "
"of missingno."
)
plt.show()
else:
return ax0


def geoplot(df,
filter=None, n=0, p=0,
x=None, y=None, figsize=(25, 10), inline=False,
by=None, cmap='YlGn', **kwargs):
"""
Generates a geographical data nullity heatmap, which shows the distribution of missing data across geographic
regions. The precise output depends on the inputs provided. If no geographical context is provided, a quadtree
is computed and nullities are rendered as abstract geographic squares. If geographical context is provided in the
form of a column of geographies (region, borough. ZIP code, etc.) in the `DataFrame`, convex hulls are computed
for each of the point groups and the heatmap is generated within them.
:param df: The DataFrame whose completeness is being geoplotted.
:param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default).
:param n: The cap on the number of columns to include in the filtered DataFrame.
:param p: The cap on the percentage fill of the columns in the filtered DataFrame.
:param figsize: The size of the figure to display. This is a `matplotlib` parameter which defaults to `(25, 10)`.
:param x: The variable in the dataset containing the x-coordinates of the dataset.
:param y: The variable in the dataset containing the y-coordinates of the dataset.
:param by: If specified, plot in convex hull mode, using the given column to cluster points in the same area. If
not specified, plot in quadtree mode.
:param cmap: The colormap to display the data with. Defaults to `YlGn`.
:param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
return its figure.
:param kwargs: Additional keyword arguments are passed to the underlying `geoplot` function.
:return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing.
"""
warnings.warn(
"The 'geoplot' function has been deprecated, and will be removed in a future version "
"of missingno. The 'geoplot' package has an example recipe for a more full-featured "
"geospatial nullity plot: "
"https://residentmario.github.io/geoplot/gallery/plot_san_francisco_trees.html"
)
try:
import geoplot as gplt
except ImportError:
raise ImportError("Install geoplot <= 0.2.4 (the package) for geoplot function support")

if gplt.__version__ >= "0.3.0":
raise ImportError(
"The missingno geoplot function requires geoplot package version 0.2.4 or lower."
"To use the geoplot function, downgrade to an older version of the geoplot package."
)

import geopandas as gpd
from shapely.geometry import Point

df = nullity_filter(df, filter=filter, n=n, p=p)

nullity = df.notnull().sum(axis='columns') / df.shape[1]
if x and y:
gdf = gpd.GeoDataFrame(nullity, columns=['nullity'],
geometry=df.apply(lambda srs: Point(srs[x], srs[y]), axis='columns'))
else:
raise ValueError("The 'x' and 'y' parameters must be specified.")

if by:
if df[by].isnull().any():
warnings.warn('The "{0}" column included null values. The offending records were dropped'.format(by))
df = df.dropna(subset=[by])
gdf = gdf.loc[df.index]

vc = df[by].value_counts()
if (vc < 3).any():
warnings.warn('Grouping by "{0}" included clusters with fewer than three points, which cannot be made '
'polygonal. The offending records were dropped.'.format(by))
where = df[by].isin((df[by].value_counts() > 2).where(lambda b: b).dropna().index.values)
gdf = gdf.loc[where]
gdf[by] = df[by]

gplt.aggplot(gdf, figsize=figsize, hue='nullity', agg=np.average, cmap=cmap, by=by, edgecolor='None', **kwargs)
ax = plt.gca()

if inline:
warnings.warn(
"The 'inline' argument has been deprecated, and will be removed in a future version "
"of missingno."
)
plt.show()
else:
return ax
return ax0

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