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Ames Housing Dataset VisualizationΒΆ
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
/home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(series[:10])
Target looks like regression
/home/circleci/project/dabl/plot/utils.py:704: UserWarning: Dropped 2 outliers in column SalePrice.
warn("Dropped {} outliers in column {}.".format(
/home/circleci/project/dabl/plot/supervised.py:652: UserWarning: Discarding 2 outliers in target column.
warn(f"Discarding {n_outliers} outliers in target column.",
/home/circleci/project/dabl/plot/supervised.py:111: UserWarning: Showing only top 10 continuous features.
warn("Showing only top 10 continuous features.")
Showing only top 10 of 41 categorical features
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
/home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
medians = X_new.groupby(col)[target_col].median()
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped_vals = vals.groupby(grouper)
# sphinx_gallery_thumbnail_number = 3
from dabl import plot
from dabl.datasets import load_ames
import matplotlib.pyplot as plt
# load the ames housing dataset
# returns a plain dataframe
data = load_ames()
plot(data, target_col='SalePrice')
plt.show()
Total running time of the script: (0 minutes 15.298 seconds)