You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
203 lines
6.7 KiB
Python
203 lines
6.7 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING
|
|
|
|
import numpy as np
|
|
|
|
import pandas as pd
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Iterable
|
|
|
|
|
|
class TablePlotter:
|
|
"""
|
|
Layout some DataFrames in vertical/horizontal layout for explanation.
|
|
Used in merging.rst
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
cell_width: float = 0.37,
|
|
cell_height: float = 0.25,
|
|
font_size: float = 7.5,
|
|
) -> None:
|
|
self.cell_width = cell_width
|
|
self.cell_height = cell_height
|
|
self.font_size = font_size
|
|
|
|
def _shape(self, df: pd.DataFrame) -> tuple[int, int]:
|
|
"""
|
|
Calculate table shape considering index levels.
|
|
"""
|
|
row, col = df.shape
|
|
return row + df.columns.nlevels, col + df.index.nlevels
|
|
|
|
def _get_cells(self, left, right, vertical) -> tuple[int, int]:
|
|
"""
|
|
Calculate appropriate figure size based on left and right data.
|
|
"""
|
|
if vertical:
|
|
# calculate required number of cells
|
|
vcells = max(sum(self._shape(df)[0] for df in left), self._shape(right)[0])
|
|
hcells = max(self._shape(df)[1] for df in left) + self._shape(right)[1]
|
|
else:
|
|
vcells = max([self._shape(df)[0] for df in left] + [self._shape(right)[0]])
|
|
hcells = sum([self._shape(df)[1] for df in left] + [self._shape(right)[1]])
|
|
return hcells, vcells
|
|
|
|
def plot(self, left, right, labels: Iterable[str] = (), vertical: bool = True):
|
|
"""
|
|
Plot left / right DataFrames in specified layout.
|
|
|
|
Parameters
|
|
----------
|
|
left : list of DataFrames before operation is applied
|
|
right : DataFrame of operation result
|
|
labels : list of str to be drawn as titles of left DataFrames
|
|
vertical : bool, default True
|
|
If True, use vertical layout. If False, use horizontal layout.
|
|
"""
|
|
from matplotlib import gridspec
|
|
import matplotlib.pyplot as plt
|
|
|
|
if not isinstance(left, list):
|
|
left = [left]
|
|
left = [self._conv(df) for df in left]
|
|
right = self._conv(right)
|
|
|
|
hcells, vcells = self._get_cells(left, right, vertical)
|
|
|
|
if vertical:
|
|
figsize = self.cell_width * hcells, self.cell_height * vcells
|
|
else:
|
|
# include margin for titles
|
|
figsize = self.cell_width * hcells, self.cell_height * vcells
|
|
fig = plt.figure(figsize=figsize)
|
|
|
|
if vertical:
|
|
gs = gridspec.GridSpec(len(left), hcells)
|
|
# left
|
|
max_left_cols = max(self._shape(df)[1] for df in left)
|
|
max_left_rows = max(self._shape(df)[0] for df in left)
|
|
for i, (_left, _label) in enumerate(zip(left, labels)):
|
|
ax = fig.add_subplot(gs[i, 0:max_left_cols])
|
|
self._make_table(ax, _left, title=_label, height=1.0 / max_left_rows)
|
|
# right
|
|
ax = plt.subplot(gs[:, max_left_cols:])
|
|
self._make_table(ax, right, title="Result", height=1.05 / vcells)
|
|
fig.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95)
|
|
else:
|
|
max_rows = max(self._shape(df)[0] for df in left + [right])
|
|
height = 1.0 / np.max(max_rows)
|
|
gs = gridspec.GridSpec(1, hcells)
|
|
# left
|
|
i = 0
|
|
for df, _label in zip(left, labels):
|
|
sp = self._shape(df)
|
|
ax = fig.add_subplot(gs[0, i : i + sp[1]])
|
|
self._make_table(ax, df, title=_label, height=height)
|
|
i += sp[1]
|
|
# right
|
|
ax = plt.subplot(gs[0, i:])
|
|
self._make_table(ax, right, title="Result", height=height)
|
|
fig.subplots_adjust(top=0.85, bottom=0.05, left=0.05, right=0.95)
|
|
|
|
return fig
|
|
|
|
def _conv(self, data):
|
|
"""
|
|
Convert each input to appropriate for table outplot.
|
|
"""
|
|
if isinstance(data, pd.Series):
|
|
if data.name is None:
|
|
data = data.to_frame(name="")
|
|
else:
|
|
data = data.to_frame()
|
|
data = data.fillna("NaN")
|
|
return data
|
|
|
|
def _insert_index(self, data):
|
|
# insert is destructive
|
|
data = data.copy()
|
|
idx_nlevels = data.index.nlevels
|
|
if idx_nlevels == 1:
|
|
data.insert(0, "Index", data.index)
|
|
else:
|
|
for i in range(idx_nlevels):
|
|
data.insert(i, f"Index{i}", data.index._get_level_values(i))
|
|
|
|
col_nlevels = data.columns.nlevels
|
|
if col_nlevels > 1:
|
|
col = data.columns._get_level_values(0)
|
|
values = [
|
|
data.columns._get_level_values(i)._values for i in range(1, col_nlevels)
|
|
]
|
|
col_df = pd.DataFrame(values)
|
|
data.columns = col_df.columns
|
|
data = pd.concat([col_df, data])
|
|
data.columns = col
|
|
return data
|
|
|
|
def _make_table(self, ax, df, title: str, height: float | None = None) -> None:
|
|
if df is None:
|
|
ax.set_visible(False)
|
|
return
|
|
|
|
from pandas import plotting
|
|
|
|
idx_nlevels = df.index.nlevels
|
|
col_nlevels = df.columns.nlevels
|
|
# must be convert here to get index levels for colorization
|
|
df = self._insert_index(df)
|
|
tb = plotting.table(ax, df, loc=9)
|
|
tb.set_fontsize(self.font_size)
|
|
|
|
if height is None:
|
|
height = 1.0 / (len(df) + 1)
|
|
|
|
props = tb.properties()
|
|
for (r, c), cell in props["celld"].items():
|
|
if c == -1:
|
|
cell.set_visible(False)
|
|
elif r < col_nlevels and c < idx_nlevels:
|
|
cell.set_visible(False)
|
|
elif r < col_nlevels or c < idx_nlevels:
|
|
cell.set_facecolor("#AAAAAA")
|
|
cell.set_height(height)
|
|
|
|
ax.set_title(title, size=self.font_size)
|
|
ax.axis("off")
|
|
|
|
|
|
def main() -> None:
|
|
import matplotlib.pyplot as plt
|
|
|
|
p = TablePlotter()
|
|
|
|
df1 = pd.DataFrame({"A": [10, 11, 12], "B": [20, 21, 22], "C": [30, 31, 32]})
|
|
df2 = pd.DataFrame({"A": [10, 12], "C": [30, 32]})
|
|
|
|
p.plot([df1, df2], pd.concat([df1, df2]), labels=["df1", "df2"], vertical=True)
|
|
plt.show()
|
|
|
|
df3 = pd.DataFrame({"X": [10, 12], "Z": [30, 32]})
|
|
|
|
p.plot(
|
|
[df1, df3], pd.concat([df1, df3], axis=1), labels=["df1", "df2"], vertical=False
|
|
)
|
|
plt.show()
|
|
|
|
idx = pd.MultiIndex.from_tuples(
|
|
[(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")]
|
|
)
|
|
column = pd.MultiIndex.from_tuples([(1, "A"), (1, "B")])
|
|
df3 = pd.DataFrame({"v1": [1, 2, 3, 4, 5, 6], "v2": [5, 6, 7, 8, 9, 10]}, index=idx)
|
|
df3.columns = column
|
|
p.plot(df3, df3, labels=["df3"])
|
|
plt.show()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|