# https://github.com/matplotlib/matplotlib/issues/5836#issuecomment-179592427
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import matplotlib.pyplot as plt
タイタニック号 災害から学ぶ機械学習#
データの取得#
Titanicのデータを取得します。
# !kaggle competitions download -c titanic --quiet
ダウンロードの際にはkaggle.jsonファイルを ~/.kaggle/kaggle.json に配置しておく必要があります。
titanic.zipファイルがダウンロードされますのでカレントディレクトリに展開します。
# !unzip titanic.zip
train.csv ファイルに訓練用のデータが保存されていますので pandas.read_csv で読み込みます。
# import pandas as pd
# train = pd.read_csv("train.csv")
# train.head(3)
test.csv ファイルにテスト用のデータが保存されていますので同様に pandas.read_csv で読み込みます。
# import pandas as pd
# test = pd.read_csv("test.csv")
# test.head(3)
また、 seaborn というライブラリを使用してもデータを取得することができます。
# see https://seaborn.pydata.org/examples/logistic_regression.html
import seaborn as sns
# Load the example Titanic dataset
df = sns.load_dataset("titanic")
# df = train
# Make a custom palette with gendered colors
pal = dict(male="#6495ED", female="#F08080")
# Show the survival probability as a function of age and sex
# g = sns.lmplot(
# x="age",
# y="survived",
# col="sex",
# hue="sex",
# data=df,
# palette=pal,
# y_jitter=0.02,
# logistic=True,
# truncate=False,
# )
g = sns.lmplot(
x="Age",
y="Survived",
col="Sex",
hue="Sex",
data=df,
palette=pal,
y_jitter=0.02,
logistic=True,
truncate=False,
)
g.set(xlim=(0, 80), ylim=(-0.05, 1.05))
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[6], line 23
9 pal = dict(male="#6495ED", female="#F08080")
11 # Show the survival probability as a function of age and sex
12 # g = sns.lmplot(
13 # x="age",
(...)
21 # truncate=False,
22 # )
---> 23 g = sns.lmplot(
24 x="Age",
25 y="Survived",
26 col="Sex",
27 hue="Sex",
28 data=df,
29 palette=pal,
30 y_jitter=0.02,
31 logistic=True,
32 truncate=False,
33 )
34 g.set(xlim=(0, 80), ylim=(-0.05, 1.05))
File ~/checkouts/readthedocs.org/user_builds/tkoyama010-notebooks/conda/latest/lib/python3.11/site-packages/seaborn/regression.py:595, in lmplot(data, x, y, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, facet_kws)
593 need_cols = [x, y, hue, col, row, units, x_partial, y_partial]
594 cols = np.unique([a for a in need_cols if a is not None]).tolist()
--> 595 data = data[cols]
597 # Initialize the grid
598 facets = FacetGrid(
599 data, row=row, col=col, hue=hue,
600 palette=palette,
(...)
603 **facet_kws,
604 )
File ~/checkouts/readthedocs.org/user_builds/tkoyama010-notebooks/conda/latest/lib/python3.11/site-packages/pandas/core/frame.py:3767, in DataFrame.__getitem__(self, key)
3765 if is_iterator(key):
3766 key = list(key)
-> 3767 indexer = self.columns._get_indexer_strict(key, "columns")[1]
3769 # take() does not accept boolean indexers
3770 if getattr(indexer, "dtype", None) == bool:
File ~/checkouts/readthedocs.org/user_builds/tkoyama010-notebooks/conda/latest/lib/python3.11/site-packages/pandas/core/indexes/base.py:5877, in Index._get_indexer_strict(self, key, axis_name)
5874 else:
5875 keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
-> 5877 self._raise_if_missing(keyarr, indexer, axis_name)
5879 keyarr = self.take(indexer)
5880 if isinstance(key, Index):
5881 # GH 42790 - Preserve name from an Index
File ~/checkouts/readthedocs.org/user_builds/tkoyama010-notebooks/conda/latest/lib/python3.11/site-packages/pandas/core/indexes/base.py:5938, in Index._raise_if_missing(self, key, indexer, axis_name)
5936 if use_interval_msg:
5937 key = list(key)
-> 5938 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
5940 not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
5941 raise KeyError(f"{not_found} not in index")
KeyError: "None of [Index(['Age', 'Sex', 'Survived'], dtype='object')] are in the [columns]"