Truth value of a Dataframe is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all() - pandas

Question

I've tried to do pairplot by seaborn with my csv data (this link) by follow code according to seaborn site.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
freq_data = pd.read_csv('C:\\Users\\frequency.csv')
freq = sns.load_dataset(freq_data)
df = sns.pairplot(iris, hue="condition", height=2.5)
plt.show()
the results show the trackback of ambiguous of dataframe
Traceback (most recent call last):
File "\.vscode\test.py", line 8, in <module>
freq = sns.load_dataset(freq_data)
File "\site-packages\seaborn\utils.py", line 485, in load_dataset
if name not in get_dataset_names():
File "\site-packages\pandas\core\generic.py", line 1441, in __nonzero__
raise ValueError(
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I've checked my data and result it here
condition area sphericity aspect_ratio
0 20 kHz 0.249 0.287 1.376
1 20 kHz 0.954 0.721 1.421
2 20 kHz 0.118 0.260 1.409
3 20 kHz 0.540 0.552 1.526
4 20 kHz 0.448 0.465 1.160
.. ... ... ... ...
310 30 kHz 6.056 0.955 2.029
311 30 kHz 4.115 1.097 1.398
312 30 kHz 11.055 1.816 1.838
313 30 kHz 4.360 1.183 1.162
314 30 kHz 10.596 0.940 1.715
[315 rows x 4 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 315 entries, 0 to 314
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 condition 315 non-null object
1 area 315 non-null float64
2 sphericity 315 non-null float64
3 aspect_ratio 315 non-null float64
dtypes: float64(3), object(1)
memory usage: 10.0+ KB
I have no ideas what happen with my dataframe :(
Please advice me to solve these problem
Thank you everyone

Answer 1

The first argument of seaborn.load_dataset() is name of the dataset ({name}.csv on https://github.com/mwaskom/seaborn-data) not a pandas.DataFrame object. The return value of seaborn.load_dataset() is just pandas.DataFrame, so you don't need to do
freq = sns.load_dataset(freq_data)
Moreover, you may want freq_data rather than iris in df = sns.pairplot(iris, hue="condition", height=2.5).
Here is the final example code
from io import StringIO
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
TESTDATA = StringIO("""condition;area;sphericity;aspect_ratio
20 kHz;0.249;0.287;1.376
20 kHz;0.954;0.721;1.421
20 kHz;0.118;0.260;1.409
20 kHz;0.540;0.552;1.526
20 kHz;0.448;0.465;1.160
30 kHz;6.056;0.955;2.029
30 kHz;4.115;1.097;1.398
30 kHz;11.055;1.816;1.838
30 kHz;4.360;1.183;1.162
30 kHz;10.596;0.940;1.715
""")
freq_data = pd.read_csv(TESTDATA, sep=";")
sns.pairplot(freq_data, hue="condition", height=2.5)
plt.show()