`size` parameter of `scatterplot` does not accept Float64 type #3519

mofojed · 2023-10-11T11:51:49Z

Create a table that has dtypes Float64 and use one of the columns for the size parameter in scatterplot:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 },
    {"X": 6, "Y": -0.279415, "Z": 0.96017, "R": 0.412779, "S": 0.837251 },
    {"X": 7, "Y": 0.656987, "Z": 0.753902, "R": 0.33618, "S": 30.597325 },
    {"X": 8, "Y": 0.989358, "Z": -0.1455, "R": 0.312757, "S": 2.10432 },
    {"X": 9, "Y": 0.412118, "Z": -0.91113, "R": 0.88594, "S": 33.09462 }
])
df_sb_multi = df_sb_multi.convert_dtypes()

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

Running that code produces the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[4], line 21
     19 fig_sb_multi, sb_multi_ax = plt.subplots()
     20 sb_multi_ax.clear()
---> 21 sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

File /lib/python3.11/site-packages/seaborn/relational.py:624, in scatterplot(data, x, y, hue, size, style, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, legend, ax, **kwargs)
    621 color = kwargs.pop("color", None)
    622 kwargs["color"] = _default_color(ax.scatter, hue, color, kwargs)
--> 624 p.plot(ax, kwargs)
    626 return ax

File /lib/python3.11/site-packages/seaborn/relational.py:458, in _ScatterPlotter.plot(self, ax, kws)
    456 if self.legend:
    457     attrs = {"hue": "color", "size": "s", "style": None}
--> 458     self.add_legend_data(ax, _scatter_legend_artist, kws, attrs)
    459     handles, _ = ax.get_legend_handles_labels()
    460     if handles:

File /lib/python3.11/site-packages/seaborn/_base.py:1246, in VectorPlotter.add_legend_data(self, ax, func, common_kws, attrs, semantic_kws)
   1244     attrs = {"hue": "color", "size": ["linewidth", "s"], "style": None}
   1245 for var, names in attrs.items():
-> 1246     self._update_legend_data(
   1247         update, var, verbosity, title, title_kws, names, semantic_kws.get(var),
   1248     )
   1250 legend_data = {}
   1251 legend_order = []

File /lib/python3.11/site-packages/seaborn/_base.py:1313, in VectorPlotter._update_legend_data(self, update, var, verbosity, title, title_kws, attr_names, other_props)
   1311         locator = mpl.ticker.MaxNLocator(nbins=brief_ticks)
   1312     limits = min(mapper.levels), max(mapper.levels)
-> 1313     levels, formatted_levels = locator_to_legend_entries(
   1314         locator, limits, self.plot_data[var].infer_objects().dtype
   1315     )
   1316 elif mapper.levels is None:
   1317     levels = formatted_levels = []

File /lib/python3.11/site-packages/seaborn/utils.py:698, in locator_to_legend_entries(locator, limits, dtype)
    696 def locator_to_legend_entries(locator, limits, dtype):
    697     """Return levels and formatted levels for brief numeric legends."""
--> 698     raw_levels = locator.tick_values(*limits).astype(dtype)
    700     # The locator can return ticks outside the limits, clip them here
    701     raw_levels = [l for l in raw_levels if l >= limits[0] and l <= limits[1]]

TypeError: Cannot interpret 'Float64Dtype()' as a data type

If you omit the size parameter or explicitly convert the types to float32, it works, e.g.:

df_sb_multi = df_sb_multi.astype({
    column: np.float32
    for column in df_sb_multi.drop(["X"], axis=1).columns
})

Does seaborn not support Float64 type?

The text was updated successfully, but these errors were encountered:

mwaskom · 2023-10-11T14:54:21Z

To clarify, the important distinction is not between 32/64 bit precision, but between float/Float, where the latter is the pandas-specific dtype rather than a dtype that also exists in numpy.

alexpeters1208 · 2023-10-11T15:10:47Z

Some additional detail that we just found...
This script does produce the above error:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 },
    {"X": 6, "Y": -0.279415, "Z": 0.96017, "R": 0.412779, "S": 0.837251 }
])
df_sb_multi = df_sb_multi.convert_dtypes()

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

but this one does not:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 }
])
df_sb_multi = df_sb_multi.convert_dtypes()

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

There is a hard threshold between plotting 6 and 7 points where this error starts.

mwaskom · 2023-10-11T16:31:11Z

That makes sense, the error is being raised from matplotlib ticker code that is producing the “brief” legend values.

Ultimately the pandas dtypes are an ongoing annoyance for seaborn. They’re still “experimental” in pandas and often cause issues in code written expecting numpy dtypes, which is most of matplotlib. Seaborn can try to cast data types and handle specific cases where they arise but there’s not a great general solution.

mofojed · 2023-10-11T16:41:16Z

@mwaskom would the recommendation be to use numpy data types in the meantime? Are you aware of any notes in matplotlib with a similar recommendation?

mwaskom · 2023-10-15T23:47:27Z

Yes, your issue is with the Float64 types produced by convert_dtypes. Using "regular" float64 should work fine.

lavanya-naresh · 2023-11-01T20:41:52Z

Some additional detail that we just found... This script does produce the above error:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 },
    {"X": 6, "Y": -0.279415, "Z": 0.96017, "R": 0.412779, "S": 0.837251 }
])
df_sb_multi = df_sb_multi.convert_dtypes()

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

but this one does not:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 }
])
df_sb_multi = df_sb_multi.convert_dtypes()

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

There is a hard threshold between plotting 6 and 7 points where this error starts.

I tried using astype function to convert the dataframe dtypes to float64 and it seems to work for more than the threshold of 7 rows.
Version info: pandas 2.1.2, seaborn 0.13.0, matplotlib 3.7.1

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df_sb_multi = pd.DataFrame([
    {"X": 0, "Y": 0.0, "Z": 1.0, "R": 0.498653, "S": 2.582756 },
    {"X": 1, "Y": 0.841471, "Z": 0.540302, "R": 0.663367, "S": 3.193578 },
    {"X": 2, "Y": 0.909297, "Z": -0.416147, "R": 0.326006, "S": 0.241508 },
    {"X": 3, "Y": 0.14112, "Z": -0.989992, "R": 0.298382, "S": 40.054015 },
    {"X": 4, "Y": -0.756802, "Z": -0.653644, "R": 0.410429, "S": 33.189659 },
    {"X": 5, "Y": -0.958924, "Z": 0.283662, "R": 0.756501, "S": 41.980234 },
    {"X": 6, "Y": -0.279415, "Z": 0.96017, "R": 0.412779, "S": 0.837251 }
])
df_sb_multi = df_sb_multi.astype("float64")

fig_sb_multi, sb_multi_ax = plt.subplots()
sb_multi_ax.clear()
sns.scatterplot(df_sb_multi, x="X", y="R", size="S", ax=sb_multi_ax)

However, convert_dtypes() usage results in the error.

mwaskom · 2023-11-02T00:22:32Z

However, convert_dtypes() usage results in the error.

Right, convert_dtypes produces pandas types by default. You could use convert_floating=False too...

WillAyd · 2024-07-16T17:32:37Z

Ultimately the pandas dtypes are an ongoing annoyance for seaborn

I'm sorry that you have been experiencing this downstream, but I'm also not surprised. I have made a PDEP in pandas to align on a Logical Type System that I think could help, and would love any feedback on to improve this experience for the ecosystem:

pandas-dev/pandas#58455

They’re still “experimental” in pandas and often cause issues in code written expecting numpy dtypes, which is most of matplotlib

This is another area I am hoping we can address via the PDEP process:

pandas-dev/pandas#59125 (comment)

These "experimental" types have existed since 2019 with little updates on the pandas side, so the experimental label is disingenuous at this point

alexpeters1208 mentioned this issue Oct 14, 2023

Matplotlib/Seaborn bugs summary and plan deephaven/deephaven-core#4644

Open

mwaskom added the rough-edge label Nov 4, 2023

mwaskom mentioned this issue Jul 16, 2024

TypeError: Cannot interpret 'Float64Dtype()' as a data type with pandas extension types #3731

Closed

WillAyd mentioned this issue Aug 29, 2024

Use Arrow PyCapsule Interface instead of Dataframe Interchange Protocol #3756

Open

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`size` parameter of `scatterplot` does not accept Float64 type #3519

`size` parameter of `scatterplot` does not accept Float64 type #3519

mofojed commented Oct 11, 2023

mwaskom commented Oct 11, 2023 •

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alexpeters1208 commented Oct 11, 2023

mwaskom commented Oct 11, 2023

mofojed commented Oct 11, 2023

mwaskom commented Oct 15, 2023

lavanya-naresh commented Nov 1, 2023 •

edited

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mwaskom commented Nov 2, 2023

WillAyd commented Jul 16, 2024

size parameter of scatterplot does not accept Float64 type #3519

size parameter of scatterplot does not accept Float64 type #3519

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mofojed commented Oct 11, 2023

mwaskom commented Oct 11, 2023 • edited Loading

alexpeters1208 commented Oct 11, 2023

mwaskom commented Oct 11, 2023

mofojed commented Oct 11, 2023

mwaskom commented Oct 15, 2023

lavanya-naresh commented Nov 1, 2023 • edited Loading

mwaskom commented Nov 2, 2023

WillAyd commented Jul 16, 2024

`size` parameter of `scatterplot` does not accept Float64 type #3519

`size` parameter of `scatterplot` does not accept Float64 type #3519

mwaskom commented Oct 11, 2023 •

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lavanya-naresh commented Nov 1, 2023 •

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