Seaborn pairplot not running only on a specific system - pandas

Question

I have the following data with the name 'Salaries.csv'. It looks like the following:[The dataset has some columns like Index(['yearID', 'teamID', 'lgID', 'salary', 'num_feat'], dtype='object'). Please note that the column num_feat I have added to the DataFrame.
I want to do a Seaborn pairplot for team 'ATL' to plot scatter plots among all numeric features in the data frame.
I have the following code :
import seaborn as sns
var_set = [
"yearID",
"teamID",
"lgID",
"playerID",
"salary"
]
head_set = []
head_set.extend(var_set)
head_set.append("num_feat")
df = pd.read_csv('Salaries.csv',index_col='playerID', header=None, names=head_set)
df['num_feat'] = 100 * np.random.random_sample(df.shape[0]). #Adding column num_feat
df_copy = df
cols_with_team_ATL = df_copy.loc[df_copy.teamID=="ATL", ]
# Create the default pairplot
pairplot_fig = sns.pairplot(cols_with_team_ATL, vars=['yearID', 'salary', 'num_feat'])
plt.subplots_adjust(top=0.9)
pairplot_fig.fig.suptitle("Scatter plots among all numeric features in the data frame for teamID = ATL", fontsize=18, alpha=0.9, weight='bold')
plt.show()
The same code runs perfectly on my friend's system but not on mine. It shows the following error in my system :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/var/folders/ch/6r9p7n0j3xg1l79lz1zdkvsh0000gq/T/ipykernel_97373/3735184261.py in <module>
25 # Create the default pairplot
26 print(df.columns)
---> 27 pairplot_fig = sns.pairplot(cols_with_team_ATL, vars=['yearID', 'salary', 'num_feat'])
28 plt.subplots_adjust(top=0.9)
29 pairplot_fig.fig.suptitle("Scatter plots among all numeric features in the data frame for teamID = ATL", fontsize=18, alpha=0.9, weight='bold')
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py in inner_f(*args, **kwargs)
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)
47 return inner_f
48
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py in pairplot(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)
2124 diag_kws.setdefault("legend", False)
2125 if diag_kind == "hist":
-> 2126 grid.map_diag(histplot, **diag_kws)
2127 elif diag_kind == "kde":
2128 diag_kws.setdefault("fill", True)
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py in map_diag(self, func, **kwargs)
1476 plot_kwargs.setdefault("hue_order", self._hue_order)
1477 plot_kwargs.setdefault("palette", self._orig_palette)
-> 1478 func(x=vector, **plot_kwargs)
1479 ax.legend_ = None
1480
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)
1460 if p.univariate:
1461
-> 1462 p.plot_univariate_histogram(
1463 multiple=multiple,
1464 element=element,
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py in plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)
426
427 # First pass through the data to compute the histograms
--> 428 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
429
430 # Prepare the relevant data
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/_core.py in iter_data(self, grouping_vars, reverse, from_comp_data)
981
982 if from_comp_data:
--> 983 data = self.comp_data
984 else:
985 data = self.plot_data
~/USC/anaconda3/lib/python3.9/site-packages/seaborn/_core.py in comp_data(self)
1055 orig = self.plot_data[var].dropna()
1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var)
-> 1057 comp_col.loc[orig.index] = pd.to_numeric(axis.convert_units(orig))
1058
1059 if axis.get_scale() == "log":
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
721
722 iloc = self if self.name == "iloc" else self.obj.iloc
--> 723 iloc._setitem_with_indexer(indexer, value, self.name)
724
725 def _validate_key(self, key, axis: int):
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value, name)
1730 self._setitem_with_indexer_split_path(indexer, value, name)
1731 else:
-> 1732 self._setitem_single_block(indexer, value, name)
1733
1734 def _setitem_with_indexer_split_path(self, indexer, value, name: str):
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py in _setitem_single_block(self, indexer, value, name)
1966
1967 # actually do the set
-> 1968 self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
1969 self.obj._maybe_update_cacher(clear=True)
1970
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/internals/managers.py in setitem(self, indexer, value)
353
354 def setitem(self: T, indexer, value) -> T:
--> 355 return self.apply("setitem", indexer=indexer, value=value)
356
357 def putmask(self, mask, new, align: bool = True):
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
325 applied = b.apply(f, **kwargs)
326 else:
--> 327 applied = getattr(b, f)(**kwargs)
328 except (TypeError, NotImplementedError):
329 if not ignore_failures:
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/internals/blocks.py in setitem(self, indexer, value)
941
942 # length checking
--> 943 check_setitem_lengths(indexer, value, values)
944 exact_match = is_exact_shape_match(values, arr_value)
945
~/USC/anaconda3/lib/python3.9/site-packages/pandas/core/indexers.py in check_setitem_lengths(indexer, value, values)
174 and len(indexer[indexer]) == len(value)
175 ):
--> 176 raise ValueError(
177 "cannot set using a list-like indexer "
178 "with a different length than the value"
ValueError: cannot set using a list-like indexer with a different length than the value
Why is it not running particularly on my system? Is there any problem with the python version or Jupyter Notebook?
Please help.