Python数据科学入门（source code ebook） — 适合Python零基础

这篇内容主要分享一本Python数据科学的入门书籍（包含书中源码），比较适合Python的零基础学习者。本书主要介绍了 Python 在数据科学领域的基础工具，包括 IPython、Jupyter、NumPy、Pandas、Matplotlib（可视化包） 和 Scikit-Learn（机器学习）。

Python 作为科学计算的一流工具已经有几十年的历史了，它还被应用于大型数据集的分析和可视化。NumPy 可以处理同类型数组型数据，Pandas 可以处理多种类型带标签的数据，SciPy 可以解决常见的科学计算问题，Matplotlib 可以绘制可用于论文发表的可视化图形，IPython 可以实现交互式编程和快速分享代码，Scikit-Learn可以进行机器学习，当然还有许多其他工具。

书中内容：

书中源码内容：

接下来展现书中部分code与可视化内容：
==Example 1：Density-and-Contour-Plots ==

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as npdef f(x, y):
return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 40)X, Y = np.meshgrid(x, y)
Z = f(X, Y)contours = plt.contour(X, Y, Z, 3, colors='black')
plt.clabel(contours, inline=True, fontsize=8)plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',cmap='RdGy', alpha=0.5)plt.savefig('code1.png',dpi = 600, bbox_inches='tight')
plt.colorbar()

Example 2：Text-and-Annotation

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
plt.style.use('seaborn-whitegrid')
import numpy as np
import pandas as pdbirths = pd.read_csv('./births.csv')
quartiles = np.percentile(births['births'], [25, 50, 75])
mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])
births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')
births['day'] = births['day'].astype(int)
births.index = pd.to_datetime(10000 * births.year +100 * births.month +births.day, format='%Y%m%d')
births_by_date = births.pivot_table('births',[births.index.month, births.index.day])
births_by_date.index = [pd.datetime(2012, month, day)for (month, day) in births_by_date.index]# plot
fig, ax = plt.subplots(figsize=(12, 4))
births_by_date.plot(ax=ax)
# Add labels to the plot
style = dict(size=10, color='gray')
ax.text('2012-1-1', 3950, "New Year's Day", **style)
ax.text('2012-7-4', 4250, "Independence Day", ha='center', **style)
ax.text('2012-9-4', 4850, "Labor Day", ha='center', **style)
ax.text('2012-10-31', 4600, "Halloween", ha='right', **style)
ax.text('2012-11-25', 4450, "Thanksgiving", ha='center', **style)
ax.text('2012-12-25', 3850, "Christmas ", ha='right', **style)
# Label the axes
ax.set(title='USA births by day of year (1969-1988)',ylabel='average daily births')
# Format the x axis with centered month labels
ax.xaxis.set_major_locator(mpl.dates.MonthLocator())
ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'))
plt.savefig('code2.png',dpi = 600, bbox_inches='tight')

==Example 3：Histograms-and-Binnings ==

from numpy.random import beta
import matplotlib.pyplot as plt
plt.style.use('bmh')
def plot_beta_hist(ax, a, b):ax.hist(beta(a, b, size=10000), histtype="stepfilled",bins=25, alpha=0.8, density=True)
fig, ax = plt.subplots()
plot_beta_hist(ax, 10, 10)
plot_beta_hist(ax, 4, 12)
plot_beta_hist(ax, 50, 12)
plot_beta_hist(ax, 6, 55)
ax.set_title("'bmh' style sheet")
plt.savefig('code3.png',dpi = 600, bbox_inches='tight')
plt.show()

Example 4：Visualization-With-Seaborn

import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np
import pandas as pdimport seaborn as sns
sns.set()
iris = sns.load_dataset("iris")sns.pairplot(iris, hue='species', size=2.5)plt.savefig('code4.png',dpi = 600, bbox_inches='tight')
plt.show()

# source code & ebook
# https://mianbaoduo.com/o/bread/mbd-YpWVlpxq