Update code listings
This commit is contained in:
Michael Hartl
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e9030432e6
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6
Listing_11.1.txt
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6
Listing_11.1.txt
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@ -0,0 +1,6 @@
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$ cd ~/repo
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$ mkdir python_data_science
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$ cd python_data_science/
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$ python3 -m venv venv
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$ source venv/bin/activate
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(venv) $
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@ -1,19 +1,5 @@
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>>> from math import tau
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>>> from numpy.random import default_rng
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>>> rng = default_rng()
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>>> df = pd.DataFrame(
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... {
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... "Number": 1.0,
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... "String": "foo",
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... "Angles": np.linspace(0, tau, 5),
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... "Random": pd.Series(rng.standard_normal(5)),
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... "Timestamp": pd.Timestamp("20221020"),
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... "Size": pd.Categorical(["tiny", "small", "mid", "big", "huge"])
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... })
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>>> df
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Number String Angles Random Timestamp Size
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0 1.0 foo 0.000000 -1.954002 2022-10-20 tiny
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1 1.0 foo 1.570796 0.967171 2022-10-20 small
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2 1.0 foo 3.141593 -1.149739 2022-10-20 mid
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3 1.0 foo 4.712389 -0.084962 2022-10-20 big
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4 1.0 foo 6.283185 0.310634 2022-10-20 huge
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>>> x = np.linspace(0, tau, 100)
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>>> fig, (ax1, ax2) = plt.subplots(2)
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>>> fig.suptitle(r"Vertically stacked plots of $\cos\theta$ and $\sin\theta$.")
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>>> ax1.plot(x, np.cos(x))
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>>> ax2.plot(x, np.sin(x))
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@ -1,7 +1,19 @@
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>>> sizes = {"tiny": 4, "small": 8, "mid": 12, "big": 16, "huge": 24}
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>>> df["Size"].map(sizes)
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0 4
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1 8
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2 12
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3 16
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4 24
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>>> from math import tau
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>>> from numpy.random import default_rng
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>>> rng = default_rng()
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>>> df = pd.DataFrame(
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... {
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... "Number": 1.0,
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... "String": "foo",
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... "Angles": np.linspace(0, tau, 5),
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... "Random": pd.Series(rng.standard_normal(5)),
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... "Timestamp": pd.Timestamp("20221020"),
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... "Size": pd.Categorical(["tiny", "small", "mid", "big", "huge"])
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... })
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>>> df
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Number String Angles Random Timestamp Size
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0 1.0 foo 0.000000 -1.954002 2022-10-20 tiny
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1 1.0 foo 1.570796 0.967171 2022-10-20 small
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2 1.0 foo 3.141593 -1.149739 2022-10-20 mid
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3 1.0 foo 4.712389 -0.084962 2022-10-20 big
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4 1.0 foo 6.283185 0.310634 2022-10-20 huge
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@ -1,9 +1,7 @@
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>>> nobel.head()
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id firstname ... city country
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0 1 Wilhelm Conrad ... Munich Germany
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1 2 Hendrik A. ... Leiden the Netherlands
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2 3 Pieter ... Amsterdam the Netherlands
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3 4 Henri ... Paris France
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4 5 Pierre ... Paris France
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[5 rows x 20 columns]
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>>> sizes = {"tiny": 4, "small": 8, "mid": 12, "big": 16, "huge": 24}
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>>> df["Size"].map(sizes)
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0 4
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1 8
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2 12
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3 16
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4 24
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@ -1,3 +1,9 @@
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>>> nobel.loc[nobel["firstname"].str.contains("Kip")]
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id firstname surname ... name city country
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916 943 Kip S. Thorne ... LIGO/VIRGO Collaboration NaN NaN
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>>> nobel.head()
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id firstname ... city country
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0 1 Wilhelm Conrad ... Munich Germany
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1 2 Hendrik A. ... Leiden the Netherlands
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2 3 Pieter ... Amsterdam the Netherlands
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3 4 Henri ... Paris France
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4 5 Pierre ... Paris France
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[5 rows x 20 columns]
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@ -1,7 +1,3 @@
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>>> curies = nobel.loc[nobel["surname"].str.contains("Curie", na=False)]
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>>> curies
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id firstname ... city country
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4 5 Pierre ... Paris France
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5 6 Marie ... NaN NaN
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6 6 Marie ... Paris France
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191 194 Irène ... Paris France
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>>> nobel.loc[nobel["firstname"].str.contains("Kip")]
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id firstname surname ... name city country
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916 943 Kip S. Thorne ... LIGO/VIRGO Collaboration NaN NaN
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@ -1,8 +1,7 @@
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>>> laureates = nobel.groupby(["id", "firstname", "surname"])
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>>> sizes = laureates.size()
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>>> sizes[sizes > 1]
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id firstname surname
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6 Marie Curie 2
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66 John Bardeen 2
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217 Linus Pauling 2
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222 Frederick Sanger 2
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>>> curies = nobel.loc[nobel["surname"].str.contains("Curie", na=False)]
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>>> curies
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id firstname ... city country
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4 5 Pierre ... Paris France
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5 6 Marie ... NaN NaN
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6 6 Marie ... Paris France
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191 194 Irène ... Paris France
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@ -1,3 +1,8 @@
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>>> nobel.hist(column="lifespan")
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array([[<AxesSubplot:title={'center':'lifespan'}>]], dtype=object)
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>>> plt.show()
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>>> laureates = nobel.groupby(["id", "firstname", "surname"])
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>>> sizes = laureates.size()
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>>> sizes[sizes > 1]
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id firstname surname
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6 Marie Curie 2
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66 John Bardeen 2
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217 Linus Pauling 2
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222 Frederick Sanger 2
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@ -1,2 +1,3 @@
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>>> URL = "https://learnenough.s3.amazonaws.com/titanic.csv"
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>>> titanic = pd.read_csv(URL)
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>>> nobel.hist(column="lifespan")
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array([[<AxesSubplot:title={'center':'lifespan'}>]], dtype=object)
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>>> plt.show()
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@ -1,9 +1,2 @@
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>>> titanic = pd.read_csv(URL, index_col="Name")
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>>> titanic.head()
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PassengerId ... Embarked
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Name ...
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Braund, Mr. Owen Harris 1 ... S
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Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C
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Heikkinen, Miss. Laina 3 ... S
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Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S
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Allen, Mr. William Henry 5 ... S
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>>> URL = "https://learnenough.s3.amazonaws.com/titanic.csv"
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>>> titanic = pd.read_csv(URL)
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@ -1,15 +1,9 @@
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>>> titanic["Age"].notna()
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Name
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Braund, Mr. Owen Harris True
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Cumings, Mrs. John Bradley (Florence Briggs Thayer) True
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Heikkinen, Miss. Laina True
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Futrelle, Mrs. Jacques Heath (Lily May Peel) True
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Allen, Mr. William Henry True
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...
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Montvila, Rev. Juozas True
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Graham, Miss. Margaret Edith True
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Johnston, Miss. Catherine Helen "Carrie" False
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Behr, Mr. Karl Howell True
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Dooley, Mr. Patrick True
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Name: Age, Length: 891, dtype: bool
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>>> valid_ages = titanic[titanic["Age"].notna()]
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>>> titanic = pd.read_csv(URL, index_col="Name")
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>>> titanic.head()
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PassengerId ... Embarked
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Name ...
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Braund, Mr. Owen Harris 1 ... S
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Cumings, Mrs. John Bradley (Florence Briggs Tha... 2 ... C
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Heikkinen, Miss. Laina 3 ... S
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Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 ... S
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Allen, Mr. William Henry 5 ... S
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@ -1,2 +1,15 @@
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titanic[(titanic["Sex"] == "female") &
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(titanic["Pclass"] == 3)]["Survived"].mean()
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>>> titanic["Age"].notna()
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Name
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Braund, Mr. Owen Harris True
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Cumings, Mrs. John Bradley (Florence Briggs Thayer) True
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Heikkinen, Miss. Laina True
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Futrelle, Mrs. Jacques Heath (Lily May Peel) True
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Allen, Mr. William Henry True
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...
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Montvila, Rev. Juozas True
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Graham, Miss. Margaret Edith True
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Johnston, Miss. Catherine Helen "Carrie" False
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Behr, Mr. Karl Howell True
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Dooley, Mr. Patrick True
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Name: Age, Length: 891, dtype: bool
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>>> valid_ages = titanic[titanic["Age"].notna()]
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@ -1,4 +1,2 @@
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male_passengers = titanic[titanic["Sex"] == "male"]
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female_passengers = titanic[titanic["Sex"] == "female"]
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valid_male_ages = male_passengers[titanic["Age"].notna()]
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valid_female_ages = female_passengers[titanic["Age"].notna()]
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titanic[(titanic["Sex"] == "female") &
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(titanic["Pclass"] == 3)]["Survived"].mean()
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@ -1 +1,4 @@
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>>> from sklearn.linear_model import LinearRegression
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male_passengers = titanic[titanic["Sex"] == "male"]
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female_passengers = titanic[titanic["Sex"] == "female"]
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valid_male_ages = male_passengers[titanic["Age"].notna()]
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valid_female_ages = female_passengers[titanic["Age"].notna()]
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@ -1,5 +1 @@
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import GaussianNB
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from sklearn.linear_model import Perceptron
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier
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>>> from sklearn.linear_model import LinearRegression
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@ -1,7 +1,5 @@
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Model
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Score
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0.854749 Decision Tree
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0.854749 Random Forest
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0.787709 Logistic Regression
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0.770950 Naive Bayes
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0.743017 Perceptron
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import GaussianNB
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from sklearn.linear_model import Perceptron
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier
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7
Listing_11.25.py
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7
Listing_11.25.py
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@ -0,0 +1,7 @@
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Model
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Score
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0.854749 Decision Tree
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0.854749 Random Forest
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0.787709 Logistic Regression
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0.770950 Naive Bayes
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0.743017 Perceptron
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@ -1,17 +0,0 @@
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>>> a.reshape((-1, 1))
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array([[ 0],
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[ 1],
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[ 2],
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[ 3],
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[ 4],
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[ 5],
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[ 6],
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[ 7],
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[ 8],
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[ 9],
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[10],
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[11],
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[12],
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[13],
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[14],
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[15]])
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@ -1,5 +1,17 @@
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>>> np.arange(5)
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array([0, 1, 2, 3, 4])
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>>> angles = math.tau * np.arange(5) / 4
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>>> angles
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array([0. , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
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>>> a.reshape((-1, 1))
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array([[ 0],
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[ 1],
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[ 2],
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[ 3],
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[ 4],
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[ 5],
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[ 6],
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[ 7],
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[ 8],
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[ 9],
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[10],
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[11],
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[12],
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[13],
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[14],
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[15]])
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@ -1,8 +1,5 @@
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>>> math.cos(angles)
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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TypeError: only size-1 arrays can be converted to Python scalars
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>>> a = np.cos(angles)
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>>> a
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array([ 1.0000000e+00, 6.1232340e-17, -1.0000000e+00, -1.8369702e-16,
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1.0000000e+00])
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>>> np.arange(5)
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array([0, 1, 2, 3, 4])
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>>> angles = math.tau * np.arange(5) / 4
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>>> angles
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array([0. , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
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@ -1,5 +1,8 @@
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>>> a[np.isclose(a, 0)]
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array([ 6.1232340e-17, -1.8369702e-16])
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>>> a[np.isclose(a, 0)] = 0
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>>> math.cos(angles)
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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TypeError: only size-1 arrays can be converted to Python scalars
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>>> a = np.cos(angles)
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>>> a
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array([ 1., 0., -1., 0., 1.])
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array([ 1.0000000e+00, 6.1232340e-17, -1.0000000e+00, -1.8369702e-16,
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1.0000000e+00])
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@ -1,31 +1,5 @@
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from math import tau
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, tau, 100)
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fig, ax = plt.subplots()
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ax.set_xticks([0, tau/4, tau/2, 3*tau/4, tau])
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ax.set_yticks([-1, -1/2, 0, 1/2, 1])
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plt.grid(True)
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ax.set_xticklabels([r"$0$", r"$\tau/4$", r"$\tau/2$", r"$3\tau/4$", r"$\tau$"])
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ax.set_yticklabels([r"$-1$", r"$-1/2$", r"$0$", r"$1/2$", r"$1$"])
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ax.set_title("One period of cosine and sine", fontsize=16)
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ax.set_xlabel(r"$\theta$", fontsize=16)
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ax.set_ylabel(r"$f(\theta)$", fontsize=16)
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ax.annotate(r"$\cos\theta$", xy=(1.75, -0.3), xytext=(0.5, -0.75),
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arrowprops={"facecolor": "black", "width": 1}, fontsize=16)
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ax.annotate(r"$\sin\theta$", xy=(2.75, 0.5), xytext=(3.5, 0.75),
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arrowprops={"facecolor": "black", "width": 1}, fontsize=16)
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fig.set_dpi(150)
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ax.plot(x, np.cos(x), color="red", linestyle="dashed")
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ax.plot(x, np.sin(x), color="blue", linestyle="dotted")
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plt.show()
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>>> a[np.isclose(a, 0)]
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array([ 6.1232340e-17, -1.8369702e-16])
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>>> a[np.isclose(a, 0)] = 0
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>>> a
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array([ 1., 0., -1., 0., 1.])
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@ -1,15 +1,31 @@
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>>> from numpy.random import default_rng
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>>> rng = default_rng()
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>>> n_pts = 50
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>>> x = rng.standard_normal(n_pts)
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>>> x
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array([ 0.41256003, 0.67594205, 1.264653 , 1.16351491, -0.41594407,
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-0.60157015, 0.84889823, -0.59984223, 0.24374326, 0.06055498,
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-0.48512829, 1.02253594, -1.10982933, -0.40609179, 0.55076245,
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0.13046238, 0.86712869, 0.06139358, -2.26538163, 1.45785923,
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-0.56220574, -1.38775239, -2.39643977, -0.77498392, 1.16794796,
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-0.6588802 , 1.66343434, 1.57475219, -0.03374501, -0.62757059,
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-0.99378175, 0.69259747, -1.04555996, 0.62653116, -0.9042063 ,
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-0.32565268, -0.99762804, -0.4270288 , 0.69940045, -0.46574267,
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1.82225132, 0.23925201, -1.0443741 , -0.54779683, 1.17466477,
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-2.54906663, -0.31495622, 0.25224765, -1.20869217, -1.02737145])
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from math import tau
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, tau, 100)
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fig, ax = plt.subplots()
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ax.set_xticks([0, tau/4, tau/2, 3*tau/4, tau])
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ax.set_yticks([-1, -1/2, 0, 1/2, 1])
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plt.grid(True)
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ax.set_xticklabels([r"$0$", r"$\tau/4$", r"$\tau/2$", r"$3\tau/4$", r"$\tau$"])
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ax.set_yticklabels([r"$-1$", r"$-1/2$", r"$0$", r"$1/2$", r"$1$"])
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ax.set_title("One period of cosine and sine", fontsize=16)
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ax.set_xlabel(r"$\theta$", fontsize=16)
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ax.set_ylabel(r"$f(\theta)$", fontsize=16)
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ax.annotate(r"$\cos\theta$", xy=(1.75, -0.3), xytext=(0.5, -0.75),
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arrowprops={"facecolor": "black", "width": 1}, fontsize=16)
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ax.annotate(r"$\sin\theta$", xy=(2.75, 0.5), xytext=(3.5, 0.75),
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arrowprops={"facecolor": "black", "width": 1}, fontsize=16)
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fig.set_dpi(150)
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ax.plot(x, np.cos(x), color="red", linestyle="dashed")
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ax.plot(x, np.sin(x), color="blue", linestyle="dotted")
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plt.show()
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@ -1,5 +1,15 @@
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>>> x = np.linspace(0, tau, 100)
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>>> fig, (ax1, ax2) = plt.subplots(2)
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>>> fig.suptitle(r"Vertically stacked plots of $\cos\theta$ and $\sin\theta$.")
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>>> ax1.plot(x, np.cos(x))
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>>> ax2.plot(x, np.sin(x))
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>>> from numpy.random import default_rng
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>>> rng = default_rng()
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>>> n_pts = 50
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>>> x = rng.standard_normal(n_pts)
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>>> x
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array([ 0.41256003, 0.67594205, 1.264653 , 1.16351491, -0.41594407,
|
||||
-0.60157015, 0.84889823, -0.59984223, 0.24374326, 0.06055498,
|
||||
-0.48512829, 1.02253594, -1.10982933, -0.40609179, 0.55076245,
|
||||
0.13046238, 0.86712869, 0.06139358, -2.26538163, 1.45785923,
|
||||
-0.56220574, -1.38775239, -2.39643977, -0.77498392, 1.16794796,
|
||||
-0.6588802 , 1.66343434, 1.57475219, -0.03374501, -0.62757059,
|
||||
-0.99378175, 0.69259747, -1.04555996, 0.62653116, -0.9042063 ,
|
||||
-0.32565268, -0.99762804, -0.4270288 , 0.69940045, -0.46574267,
|
||||
1.82225132, 0.23925201, -1.0443741 , -0.54779683, 1.17466477,
|
||||
-2.54906663, -0.31495622, 0.25224765, -1.20869217, -1.02737145])
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
>>> reload(palindrome)
|
||||
>>> frase = palindrome.TranslatedPhrase("recognize", "reconocer")
|
||||
>>> frase.ispalidrome()
|
||||
>>> frase.ispalindrome()
|
||||
False
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
>>> reload(palindrome)
|
||||
>>> frase = palindrome.TranslatedPhrase("recognize", "reconocer")
|
||||
>>> frase.ispalidrome()
|
||||
>>> frase.ispalindrome()
|
||||
True
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user