TypeError using sns.distplot() on dataframe with one row - pandas

Question

I'm plotting subsets of a dataframe, and one subset happens to have only one row. This is the only reason I can think of for why it's causing problems. This is what it looks like:
problem_dataframe = prob_df[prob_df['Date']==7]
problem_dataframe.head()
I try to do:
sns.distplot(problem_dataframe['floatTime'])
But I get the error:
TypeError: len() of unsized object
Would someone please tell me what's causing this and how to work around it?

Answer 1

The TypeError is resolved by setting bins=1.
But that uncovers a different error, ValueError: x must be 1D or 2D, which gets triggered by an internal function in Matplotlib's hist(), called _normalize_input():
import pandas as pd
import seaborn as sns
df = pd.DataFrame(['Tue','Feb',7,'15:37:58',2017,15.6196]).T
df.columns = ['Day','Month','Date','Time','Year','floatTime']
sns.distplot(df.floatTime, bins=1)
Output:
ValueError Traceback (most recent call last)
<ipython-input-25-858df405d200> in <module>()
6 df.columns = ['Day','Month','Date','Time','Year','floatTime']
7 df.floatTime.values.astype(float)
----> 8 sns.distplot(df.floatTime, bins=1)
/home/andrew/anaconda3/lib/python3.6/site-packages/seaborn/distributions.py in distplot(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)
213 hist_color = hist_kws.pop("color", color)
214 ax.hist(a, bins, orientation=orientation,
--> 215 color=hist_color, **hist_kws)
216 if hist_color != color:
217 hist_kws["color"] = hist_color
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1890 warnings.warn(msg % (label_namer, func.__name__),
1891 RuntimeWarning, stacklevel=2)
-> 1892 return func(ax, *args, **kwargs)
1893 pre_doc = inner.__doc__
1894 if pre_doc is None:
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
6141 x = np.array([[]])
6142 else:
-> 6143 x = _normalize_input(x, 'x')
6144 nx = len(x) # number of datasets
6145
/home/andrew/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py in _normalize_input(inp, ename)
6080 else:
6081 raise ValueError(
-> 6082 "{ename} must be 1D or 2D".format(ename=ename))
6083 if inp.shape[1] < inp.shape[0]:
6084 warnings.warn(
ValueError: x must be 1D or 2D
_normalize_input() was removed from Matplotlib (it looks like sometime last year), so I guess Seaborn is referring to an older version under the hood.
You can see _normalize_input() in this old commit:
def _normalize_input(inp, ename='input'):
"""Normalize 1 or 2d input into list of np.ndarray or
a single 2D np.ndarray.
Parameters
----------
inp : iterable
ename : str, optional
Name to use in ValueError if `inp` can not be normalized
"""
if (isinstance(x, np.ndarray) or
not iterable(cbook.safe_first_element(inp))):
# TODO: support masked arrays;
inp = np.asarray(inp)
if inp.ndim == 2:
# 2-D input with columns as datasets; switch to rows
inp = inp.T
elif inp.ndim == 1:
# new view, single row
inp = inp.reshape(1, inp.shape[0])
else:
raise ValueError(
"{ename} must be 1D or 2D".format(ename=ename))
...
I can't figure out why inp.ndim!=1, though. Performing the same np.asarray().ndim on the input returns 1 as expected:
np.asarray(df.floatTime).ndim # 1
So you're facing a few obstacles if you want to make a single-valued input work with sns.distplot().
Suggested Workaround
Check for a single-element df.floatTime, and if that's the case, just use plt.hist() instead (which is what distplot goes to anyway, along with KDE):
plt.hist(df.floatTime)