D_Low的博客

numpy中的文件读取与保存import numpy as npimport pickle读取1.dst = numpy.load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding=‘ASCII’)file：文件名或者路径加文件名或者文件对象allow_pickle：为True时，可以从将pkl保存的文件读入，但是这样不好，一般默认为Falsef = open("a", mode="rb")# 用文

画布的分辨率与大小import numpy as npimport matplotlib.pyplot as plt%matplotlib inline默认的画布plt.plot()设置分辨率plt.rcParams['figure.dpi'] = 100plt.plot()设置图片尺寸plt.rcParams['figure.figsize'] = (8.0, 4.0)plt.plot()...

创建图形对象import numpy as npimport matplotlib.pyplot as plt%matplotlib inlinex = np.linspace(-10, 10)y = x**2fig = plt.figure('f1', figsize=(6, 4), dpi=50, facecolor='gray', frameon=True)plt.plot(x, y)绘制多子图figure 是绘制对象（可理解为一个空白的画布），一个 figure 对

坐标轴设置import matplotlib.pyplot as pltimport numpy as np%matplotlib inline左边的视图里有四条边，称为视图的spine——脊柱。如何对spine操作得到右图的效果？① 去除上面和右面的spine；②再把左面的spine移至中间；③ 再把下面的spine 向上移，使得两个0值重合所有对spine的操作均在 gca() 方法中完成， gca——get current axesx = np.linspace(-50, 51)

图例 legend()import numpy as npimport matplotlib.pyplot as plt%matplotlib inline什么是图例？图例是集中于图的一角或者一侧的各种符号和颜色代表内容的说明，有助于更好地理解图设置方法①直接在plot中设置label属性值 最后调用legend() 方法即可x = np.linspace(-5, 5)y = np.random.randint(10, 40, len(x))z = np.random.randint(

中文字体设置import numpy as npimport matplotlib.pyplot as plt%matplotlib inlinex = np.linspace(-5, 5)y = np.sin(x)plt.title('y等于sinx')plt.plot(x, y)[<matplotlib.lines.Line2D at 0x2088ed04ac0>]D:\Anaconda3\Install\lib\site-packages\IPython\co

风格设置import numpy as npimport matplotlib.pyplot as plt%matplotlib inline查看能使用哪些风格plt.style.available['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeig

基本设置import numpy as npimport matplotlib.pyplot as plt%matplotlib inline图的外观设置plt.plot([1,2,3,4], [10, 22, 32, 6]) # 每一个轴传进去的都是一个列表xlabel 和 ylabel 分别设定轴的名字 并通过 fontsize 属性设置字体大小plt.xlabel('X', fontsize=12) plt.ylabel('Y', fontsize=

字符串操作import pandas as pdimport numpy as nps = pd.Series(['A', 'c', 'AGeR', 'SEW', np.nan])s0 A1 c2 AGeR3 SEW4 NaNdtype: object字符串型 Series 的大小写转换s.str.upper()0 A1 C2 AGER3 SEW4 NaNdtype: ob

常用操作import pandas as pdimport numpy as npexample = pd.DataFrame({'Amount': [74., 235., 175., 100., 115., 245., 180., 90., 88., 129., 273., 300.], 'Category': ['Transportation', 'Grocery', 'Household', 'Entertainment',

数据透视表import pandas as pdexample = pd.DataFrame({'Amount': [74., 235., 175., 100., 115., 245., 180., 90., 88., 129., 273., 300.], 'Category': ['Transportation', 'Grocery', 'Household', 'Entertainment', 'Transport

显示设置import pandas as pddf = pd.read_csv('../../datasets/titanic/test.csv')df.head(80) PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked

对象的增删改查import pandas as pdimport numpy as np手动方法构造Seriesdata = np.arange(10)index = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']s = pd.Series(data, index)sa 0b 1c 2d 3e 4f 5g 6h 7i 8j 9dtype: int32

数值运算操作import pandas as pd可自己传入数据，指定索引名和属性名data = pd.DataFrame([[1, 2, 3], [4, 5, 6]], index=['a', 'b'], columns=['A', 'B', 'C'])data A B C a 1 2 3 b 4

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