<jupyter_start><jupyter_text>**Define contrived series**<jupyter_code>data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
series = Series(data)
print(series)<jupyter_output>0     10.0
1     20.0
2     30.0
3     40.0
4     50.0
5     60.0
6     70.0
7     80.0
8     90.0
9    100.0
dtype: float64<jupyter_text>**prepeare data for normalization**<jupyter_code>values = series.values
values = values.reshape((len(values), 1))
print(values)<jupyter_output>[[ 10.]
 [ 20.]
 [ 30.]
 [ 40.]
 [ 50.]
 [ 60.]
 [ 70.]
 [ 80.]
 [ 90.]
 [100.]]<jupyter_text>**train the normalization**<jupyter_code>scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(values)
print('Min: %f, Max: %f' % (scaler.data_min_, scaler.data_max_))<jupyter_output>Min: 10.000000, Max: 100.000000<jupyter_text>** normalize the dataset and print **<jupyter_code>normalized= scaler.transform(values)
print(normalized)<jupyter_output>[[0.        ]
 [0.11111111]
 [0.22222222]
 [0.33333333]
 [0.44444444]
 [0.55555556]
 [0.66666667]
 [0.77777778]
 [0.88888889]
 [1.        ]]<jupyter_text>** inverse transform and print **<jupyter_code>inversed = scaler.inverse_transform(normalized)
print(inversed)<jupyter_output>[[ 10.]
 [ 20.]
 [ 30.]
 [ 40.]
 [ 50.]
 [ 60.]
 [ 70.]
 [ 80.]
 [ 90.]
 [100.]]
{'fraction_non_alphanumeric': 0.18847352024922118, 'fraction_numerical': 0.16355140186915887, 'mean_word_length': 3.7769516728624537, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 8, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '192374', 'license': 'MIT', 'n_tokens_mistral': 697, 'n_tokens_neox': 556, 'n_words': 105, 'path': 'Lesson-03/.ipynb_checkpoints/Prepare Numeric Data-checkpoint.ipynb', 'repo_name': 'cbrom/LSTM-with-python', 'size': '1284'}