y, Matplotlib
Una biblioteca de Python para dibujar gráficos bidimensionales, realizar visualizaciones de datos, ayudar a comprender los datos y elegir convenientemente un método de análisis más apropiado
1. Gráfico de líneas
1.1 Introducción a matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure()
plt.plot([1, 0, 9], [4, 5, 6])
plt.show()
1.2 Dibujo y visualización del gráfico de líneas
# 展现上海一周的天气,比如从星期一到星期日的天气温度如下
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 2、绘制图像
plt.plot([1, 2, 3, 4, 5, 6, 7], [17, 17, 18, 15, 11, 11, 13])
# 保存图像
plt.savefig("test78.png")
# 3、显示图像
plt.show()
1.3 Mejorar el gráfico de líneas original 1 (capa de visualización auxiliar)
# 需求：画出某城市11点到12点1小时内每分钟的温度变化折线图，温度范围在15度~18度
import random
# 1、准备数据 x y
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制图像
plt.plot(x, y_shanghai)
# 修改x、y刻度
# 准备x的刻度说明
x_label = ["11点{}分".format(i) for i in x]
plt.xticks(x[::5], x_label[::5])
plt.yticks(range(0, 40, 5))
# 添加网格显示
plt.grid(linestyle="--", alpha=0.5)
# 添加描述信息
plt.xlabel("时间变化")
plt.ylabel("温度变化")
plt.title("某城市11点到12点每分钟的温度变化状况")
# 4、显示图
plt.show()
1.4 Mejorar el gráfico de líneas original 2 (capa de imagen)
# 需求：再添加一个城市的温度变化
# 收集到北京当天温度变化情况，温度在1度到3度。
# 1、准备数据 x y
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
y_beijing = [random.uniform(1, 3) for i in x]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制图像
plt.plot(x, y_shanghai, color="r", linestyle="-.", label="上海")
plt.plot(x, y_beijing, color="b", label="北京")
# 显示图例
plt.legend()
# 修改x、y刻度
# 准备x的刻度说明
x_label = ["11点{}分".format(i) for i in x]
plt.xticks(x[::5], x_label[::5])
plt.yticks(range(0, 40, 5))
# 添加网格显示
plt.grid(linestyle="--", alpha=0.5)
# 添加描述信息
plt.xlabel("时间变化")
plt.ylabel("温度变化")
plt.title("上海、北京11点到12点每分钟的温度变化状况")
# 4、显示图
plt.show()
1.5 Visualización de múltiples sistemas de coordenadas - plt.subplots (método de dibujo orientado a objetos)
# 需求：再添加一个城市的温度变化
# 收集到北京当天温度变化情况，温度在1度到3度。
# 1、准备数据 x y
x = range(60)
y_shanghai = [random.uniform(15, 18) for i in x]
y_beijing = [random.uniform(1, 3) for i in x]
# 2、创建画布
# plt.figure(figsize=(20, 8), dpi=80)
figure, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8), dpi=80)
# 3、绘制图像
axes[0].plot(x, y_shanghai, color="r", linestyle="-.", label="上海")
axes[1].plot(x, y_beijing, color="b", label="北京")
# 显示图例
axes[0].legend()
axes[1].legend()
# 修改x、y刻度
# 准备x的刻度说明
x_label = ["11点{}分".format(i) for i in x]
axes[0].set_xticks(x[::5])
axes[0].set_xticklabels(x_label)
axes[0].set_yticks(range(0, 40, 5))
axes[1].set_xticks(x[::5])
axes[1].set_xticklabels(x_label)
axes[1].set_yticks(range(0, 40, 5))
# 添加网格显示
axes[0].grid(linestyle="--", alpha=0.5)
axes[1].grid(linestyle="--", alpha=0.5)
# 添加描述信息
axes[0].set_xlabel("时间变化")
axes[0].set_ylabel("温度变化")
axes[0].set_title("上海11点到12点每分钟的温度变化状况")
axes[1].set_xlabel("时间变化")
axes[1].set_ylabel("温度变化")
axes[1].set_title("北京11点到12点每分钟的温度变化状况")
# 4、显示图
plt.show()
2. Dibujar una imagen de función matemática
import numpy as np
# 1、准备x，y数据
x = np.linspace(-1, 1, 1000)
y = 2 * x * x
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制图像
plt.plot(x, y)
# 添加网格显示
plt.grid(linestyle="--", alpha=0.5)
# 4、显示图像
plt.show()
2.1 Dibujo de diagrama de dispersión
# 需求：探究房屋面积和房屋价格的关系
# 1、准备数据
x = [225.98, 247.07, 253.14, 457.85, 241.58, 301.01, 20.67, 288.64,
163.56, 120.06, 207.83, 342.75, 147.9 , 53.06, 224.72, 29.51,
21.61, 483.21, 245.25, 399.25, 343.35]
y = [196.63, 203.88, 210.75, 372.74, 202.41, 247.61, 24.9 , 239.34,
140.32, 104.15, 176.84, 288.23, 128.79, 49.64, 191.74, 33.1 ,
30.74, 400.02, 205.35, 330.64, 283.45]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制图像
plt.scatter(x, y)
# 4、显示图像
plt.show()
2.2 Requisito 1- Comparar los ingresos de taquilla de cada película
# 1、准备数据
movie_names = ['雷神3：诸神黄昏','正义联盟','东方快车谋杀案','寻梦环游记','全球风暴', '降魔传','追捕','七十七天','密战','狂兽','其它']
tickets = [73853,57767,22354,15969,14839,8725,8716,8318,7916,6764,52222]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制柱状图
x_ticks = range(len(movie_names))
plt.bar(x_ticks, tickets, color=['b','r','g','y','c','m','y','k','c','g','b'])
# 修改x刻度
plt.xticks(x_ticks, movie_names)
# 添加标题
plt.title("电影票房收入对比")
# 添加网格显示
plt.grid(linestyle="--", alpha=0.5)
# 4、显示图像
plt.show()
2.3 Requisito 2- ¿Cómo comparar los ingresos de taquilla de películas de manera más convincente?
# 1、准备数据
movie_name = ['雷神3：诸神黄昏','正义联盟','寻梦环游记']
first_day = [10587.6,10062.5,1275.7]
first_weekend=[36224.9,34479.6,11830]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制柱状图
plt.bar(range(3), first_day, width=0.2, label="首日票房")
plt.bar([0.2, 1.2, 2.2], first_weekend, width=0.2, label="首周票房")
# 显示图例
plt.legend()
# 修改刻度
plt.xticks([0.1, 1.1, 2.1], movie_name)
# 4、显示图像
plt.show()
3. Dibujo de histograma
# 需求：电影时长分布状况
# 1、准备数据
time = [131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111,78, 132, 124, 113, 150, 110, 117, 86, 95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137, 92,121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113,134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112, 83, 94, 146, 133, 101,131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制直方图
distance = 2
group_num = int((max(time) - min(time)) / distance)
plt.hist(time, bins=group_num, density=True)
# 修改x轴刻度
plt.xticks(range(min(time), max(time) + 2, distance))
# 添加网格
plt.grid(linestyle="--", alpha=0.5)
# 4、显示图像
plt.show()
4. Dibujo de gráfico circular
# 1、准备数据
movie_name = ['雷神3：诸神黄昏','正义联盟','东方快车谋杀案','寻梦环游记','全球风暴','降魔传','追捕','七十七天','密战','狂兽','其它']
place_count = [60605,54546,45819,28243,13270,9945,7679,6799,6101,4621,20105]
# 2、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 3、绘制饼图
plt.pie(place_count, labels=movie_name, colors=['b','r','g','y','c','m','y','k','c','g','y'], autopct="%1.2f%%")
# 显示图例
plt.legend()
plt.axis('equal')
# 4、显示图像
plt.show()
dos, entumecido
Numpy es una herramienta informática eficiente, el núcleo es la informática ndarray
operación lógica
Cálculo estadístico
Operaciones entre arreglos
Fusionar, dividir, operaciones IO, procesamiento de datos
1. método básico ndarray
import numpy as np
score = np.array([[80, 89, 86, 67, 79],
[78, 97, 89, 67, 81],
[90, 94, 78, 67, 74],
[91, 91, 90, 67, 69],
[76, 87, 75, 67, 86],
[70, 79, 84, 67, 84],
[94, 92, 93, 67, 64],
[86, 85, 83, 67, 80]])
scorematriz ([[80, 89, 86, 67, 79],
[78, 97, 89, 67, 81],
[90, 94, 78, 67, 74],
[91, 91, 90, 67, 69] ,
[76, 87, 75, 67, 86],
[70, 79, 84, 67, 84],
[94, 92, 93, 67, 64],
[86, 85, 83, 67, 80]])
type(score)numpy.ndarray
2.1 Comparación de la eficiencia de operación de listas nativas de ndarray y Python
import random
import time
# 生成一个大数组
python_list = []
for i in range(100000000):
python_list.append(random.random())
ndarray_list = np.array(python_list)
len(ndarray_list)100000000
# 原生pythonlist求和
t1 = time.time()
a = sum(python_list)
t2 = time.time()
d1 = t2 - t1
# ndarray求和
t3 = time.time()
b = np.sum(ndarray_list)
t4 = time.time()
d2 = t4 - t3
d10.7309620380401611
d20.12980318069458008
2.2 Propiedades de ndarray
scorearray([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, 67, 86], [70, 79, 84, 67, 84], [94, 92, 93, 67, 64], [86, 85, 83, 67, 80]])
score.shape # (8, 5)(8, 5)
score.ndim2
score.size40
score.dtype dtype('int64')
score.itemsize8
2.3、ndarray的形状
a = np.array([[1,2,3],[4,5,6]])
b = np.array([1,2,3,4])
c = np.array([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
a # (2, 3)array([[1, 2, 3], [4, 5, 6]])
b # (4,)array([1, 2, 3, 4])
c # (2, 2, 3)array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
a.shape(2, 3)
b.shape(4,)
c.shape(2, 2, 3)
2.4、ndarray的类型
data = np.array([1.1, 2.2, 3.3])
dataarray([1.1, 2.2, 3.3])
data.dtypedtype('float64')
# 创建数组的时候指定类型
np.array([1.1, 2.2, 3.3], dtype="float32")array([1.1, 2.2, 3.3], dtype=float32)
np.array([1.1, 2.2, 3.3], dtype=np.float32)array([1.1, 2.2, 3.3], dtype=float32)
2、生成数组的方法
# 1 生成0和1的数组
np.zeros(shape=(3, 4), dtype="float32")array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], dtype=float32)
np.ones(shape=[2, 3], dtype=np.int32)array([[1, 1, 1], [1, 1, 1]], dtype=int32)
2.1、 从现有数组生成
scorearray([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, 67, 86], [70, 79, 84, 67, 84], [94, 92, 93, 67, 64], [86, 85, 83, 67, 80]])
# np.array()
data1 = np.array(score)
data1array([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, 67, 86], [70, 79, 84, 67, 84], [94, 92, 93, 67, 64], [86, 85, 83, 67, 80]])
# np.asarray()
data2 = np.asarray(score)
data2array([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, 67, 86], [70, 79, 84, 67, 84], [94, 92, 93, 67, 64], [86, 85, 83, 67, 80]])
# np.copy()
data3 = np.copy(score)
data3array([[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, 67, 86], [70, 79, 84, 67, 84], [94, 92, 93, 67, 64], [86, 85, 83, 67, 80]])
score[3, 1] = 10000
scorearray([[ 80, 89, 86, 67, 79], [ 78, 97, 89, 67, 81], [ 90, 94, 78, 67, 74], [ 91, 10000, 90, 67, 69], [ 76, 87, 75, 67, 86], [ 70, 79, 84, 67, 84], [ 94, 92, 93, 67, 64], [ 86, 85, 83, 67, 80]])
2.2、生成固定范围的数组
np.linspace(0, 10, 5)array([ 0. , 2.5, 5. , 7.5, 10. ])
np.arange(0, 11, 5)array([ 0, 5, 10])
2.3、生成随机数组
data1 = np.random.uniform(low=-1, high=1, size=1000000)
data1array([-0.49795073, -0.28524454, 0.56473937, ..., 0.6141957 , 0.4149972 , 0.89473129])
import matplotlib.pyplot as plt
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 2、绘制直方图
plt.hist(data1, 1000)
# 3、显示图像
plt.show()
# 正态分布
data2 = np.random.normal(loc=1.75, scale=0.1, size=1000000)
data2array([1.66381498, 1.81276401, 1.58393696, ..., 1.72017482, 1.90260969, 1.69554529])
# 1、创建画布
plt.figure(figsize=(20, 8), dpi=80)
# 2、绘制直方图
plt.hist(data2, 1000)
# 3、显示图像
plt.show()
2.4、案例：随机生成8只股票2周的交易日涨幅数据
stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
stock_changearray([[-0.03469926, 1.68760014, 0.05915316, 2.4473136 , -0.61776756,
-0.56253866, -1.24738637, 0.48320978, 1.01227938, -1.44509723],
[-1.8391253 , -1.10142576, 0.09582268, 1.01589092, -1.20262068,
0.76134643, -0.76782097, -1.11192773, 0.81609586, 0.07659056],
[-0.74293074, -0.7836588 , 1.32639574, -0.52735663, 1.4167841 ,
2.10286726, -0.21687665, -0.33073563, -0.46648617, 0.07926839],
[ 0.45914676, -0.78330377, -1.10763289, 0.10612596, -0.63375855,
-1.88121415, 0.6523779 , -1.27459184, -0.1828502 , -0.76587891],
[-0.50413407, -1.35848099, -2.21633535, -1.39300681, 0.13159471,
0.65429138, 0.32207255, 1.41792558, 1.12357799, -0.68599018],
[ 0.3627785 , 1.00279706, -0.68137875, -2.14800075, -2.82895231,
-1.69360338, 1.43816168, -2.02116677, 1.30746801, 1.41979011],
[-2.93762047, 0.22199761, 0.98788788, 0.37899235, 0.28281886,
-1.75837237, -0.09262863, -0.92354076, 1.11467277, 0.76034531],
[-0.39473551, 0.28402164, -0.15729195, -0.59342945, -1.0311294 ,
-1.07651428, 0.18618331, 1.5780439 , 1.31285558, 0.10777784]])# 获取第一个股票的前3个交易日的涨跌幅数据
stock_change[0, :3]array([-0.03469926, 1.68760014, 0.05915316])
a1 = np.array([ [[1,2,3],[4,5,6]], [[12,3,34],[5,6,7]]])
a1 # (2, 2, 3)array([[[ 1, 2, 3], [ 4, 5, 6]], [[12, 3, 34], [ 5, 6, 7]]])
a1.shape(2, 2, 3)
a1[1, 0, 2] = 100000
a1array([[[ 1, 2, 3], [ 4, 5, 6]], [[ 12, 3, 100000], [ 5, 6, 7]]])
2.5、形状修改
# 需求：让刚才的股票行、日期列反过来，变成日期行，股票列
stock_changearray([[-0.03469926, 1.68760014, 0.05915316, 2.4473136 , -0.61776756, -0.56253866, -1.24738637, 0.48320978, 1.01227938, -1.44509723], [-1.8391253 , -1.10142576, 0.09582268, 1.01589092, -1.20262068, 0.76134643, -0.76782097, -1.11192773, 0.81609586, 0.07659056], [-0.74293074, -0.7836588 , 1.32639574, -0.52735663, 1.4167841 , 2.10286726, -0.21687665, -0.33073563, -0.46648617, 0.07926839], [ 0.45914676, -0.78330377, -1.10763289, 0.10612596, -0.63375855, -1.88121415, 0.6523779 , -1.27459184, -0.1828502 , -0.76587891], [-0.50413407, -1.35848099, -2.21633535, -1.39300681, 0.13159471, 0.65429138, 0.32207255, 1.41792558, 1.12357799, -0.68599018], [ 0.3627785 , 1.00279706, -0.68137875, -2.14800075, -2.82895231, -1.69360338, 1.43816168, -2.02116677, 1.30746801, 1.41979011], [-2.93762047, 0.22199761, 0.98788788, 0.37899235, 0.28281886, -1.75837237, -0.09262863, -0.92354076, 1.11467277, 0.76034531], [-0.39473551, 0.28402164, -0.15729195, -0.59342945, -1.0311294 , -1.07651428, 0.18618331, 1.5780439 , 1.31285558, 0.10777784]])
stock_change.reshape((10, 8))
stock_change.resize((10, 8))
stock_change.Tarray([[-0.03469926, 1.68760014, 0.05915316, 2.4473136 , -0.61776756, -0.56253866, -1.24738637, 0.48320978], [ 1.01227938, -1.44509723, -1.8391253 , -1.10142576, 0.09582268, 1.01589092, -1.20262068, 0.76134643], [-0.76782097, -1.11192773, 0.81609586, 0.07659056, -0.74293074, -0.7836588 , 1.32639574, -0.52735663], [ 1.4167841 , 2.10286726, -0.21687665, -0.33073563, -0.46648617, 0.07926839, 0.45914676, -0.78330377], [-1.10763289, 0.10612596, -0.63375855, -1.88121415, 0.6523779 , -1.27459184, -0.1828502 , -0.76587891], [-0.50413407, -1.35848099, -2.21633535, -1.39300681, 0.13159471, 0.65429138, 0.32207255, 1.41792558], [ 1.12357799, -0.68599018, 0.3627785 , 1.00279706, -0.68137875, -2.14800075, -2.82895231, -1.69360338], [ 1.43816168, -2.02116677, 1.30746801, 1.41979011, -2.93762047, 0.22199761, 0.98788788, 0.37899235], [ 0.28281886, -1.75837237, -0.09262863, -0.92354076, 1.11467277, 0.76034531, -0.39473551, 0.28402164], [-0.15729195, -0.59342945, -1.0311294 , -1.07651428, 0.18618331, 1.5780439 , 1.31285558, 0.10777784]])
stock_change.astype("int32")array([[ 0, 1, 0, 2, 0, 0, -1, 0, 1, -1], [-1, -1, 0, 1, -1, 0, 0, -1, 0, 0], [ 0, 0, 1, 0, 1, 2, 0, 0, 0, 0], [ 0, 0, -1, 0, 0, -1, 0, -1, 0, 0], [ 0, -1, -2, -1, 0, 0, 0, 1, 1, 0], [ 0, 1, 0, -2, -2, -1, 1, -2, 1, 1], [-2, 0, 0, 0, 0, -1, 0, 0, 1, 0], [ 0, 0, 0, 0, -1, -1, 0, 1, 1, 0]], dtype=int32)
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3、数组的运算
3.1、数组去重
temp = np.array([[1, 2, 3, 4],[3, 4, 5, 6]])
temparray([[1, 2, 3, 4], [3, 4, 5, 6]])
np.unique(temp)array([1, 2, 3, 4, 5, 6])
set(temp.flatten()), 2, 3, 4, 5, 6}
3.2、逻辑运算
stock_change = np.random.normal(loc=0, scale=1, size=(8, 10))
stock_changearray([[ 1.46338968, -0.45576704, 0.29667843, 0.16606916, 0.46446682, 0.83167611, -1.35770374, -0.65001192, 1.38319911, -0.93415832], [ 0.36775845, 0.24078108, 0.122042 , 1.19314047, 1.34072589, 0.09361683, 1.19030379, 1.4371421 , -0.97829363, -0.11962767], [-1.48252741, -0.69347186, 0.91122464, -0.30606473, 0.41598897, 0.79542753, -0.01447862, -1.49943117, -0.23285809, 0.42806777], [ 0.39438905, -1.31770556, 1.7344868 , -1.52812773, -0.47703227, -0.3795497 , -0.88422651, 1.37510973, -0.93622775, 0.49257673], [-0.9822216 , -1.09482936, -0.81834523, 0.57335311, 0.97390091, 0.05314952, -0.58316743, 0.19264426, 0.02081861, 0.84445247], [ 0.41739964, -0.26826893, -0.70003442, -0.58593912, 0.86546709, -1.30304864, 0.05254567, -1.73976785, -0.43532247, 0.4760526 ], [-0.21739882, 0.52007085, -0.60160491, 0.57108639, 1.03303301, -0.69172579, 1.04716985, -0.22985706, -0.11125069, 0.87722923], [-0.183266 , 0.56273065, 0.29357786, -0.19343363, -1.54547303, -0.31977163, -0.00659025, 0.48160678, 0.88443604, -0.48456825]])
# 逻辑判断, 如果涨跌幅大于0.5就标记为True 否则为False
stock_change > 0.5array([[ True, False, False, False, False, True, False, False, True, False], [False, False, False, True, True, False, True, True, False, False], [False, False, True, False, False, True, False, False, False, False], [False, False, True, False, False, False, False, True, False, False], [False, False, False, True, True, False, False, False, False, True], [False, False, False, False, True, False, False, False, False, False], [False, True, False, True, True, False, True, False, False, True], [False, True, False, False, False, False, False, False, True, False]])
stock_change[stock_change > 0.5] = 1.1
stock_changearray([[ 1.1 , -0.45576704, 0.29667843, 0.16606916, 0.46446682, 1.1 , -1.35770374, -0.65001192, 1.1 , -0.93415832], [ 0.36775845, 0.24078108, 0.122042 , 1.1 , 1.1 , 0.09361683, 1.1 , 1.1 , -0.97829363, -0.11962767], [-1.48252741, -0.69347186, 1.1 , -0.30606473, 0.41598897, 1.1 , -0.01447862, -1.49943117, -0.23285809, 0.42806777], [ 0.39438905, -1.31770556, 1.1 , -1.52812773, -0.47703227, -0.3795497 , -0.88422651, 1.1 , -0.93622775, 0.49257673], [-0.9822216 , -1.09482936, -0.81834523, 1.1 , 1.1 , 0.05314952, -0.58316743, 0.19264426, 0.02081861, 1.1 ], [ 0.41739964, -0.26826893, -0.70003442, -0.58593912, 1.1 , -1.30304864, 0.05254567, -1.73976785, -0.43532247, 0.4760526 ], [-0.21739882, 1.1 , -0.60160491, 1.1 , 1.1 , -0.69172579, 1.1 , -0.22985706, -0.11125069, 1.1 ], [-0.183266 , 1.1 , 0.29357786, -0.19343363, -1.54547303, -0.31977163, -0.00659025, 0.48160678, 1.1 , -0.48456825]])
# 判断stock_change[0:2, 0:5]是否全是上涨的
stock_change[0:2, 0:5] > 0array([[ True, False, True, True, True], [ True, True, True, True, True]])
np.all(stock_change[0:2, 0:5] > 0)False
# 判断前5只股票这段期间是否有上涨的
np.any(stock_change[:5, :] > 0)True
3.3、np.where（三元运算符）
# 判断前四个股票前四天的涨跌幅 大于0的置为1，否则为0
temp = stock_change[:4, :4]
temparray([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ], [-1.48252741, -0.69347186, 1.1 , -0.30606473], [ 0.39438905, -1.31770556, 1.1 , -1.52812773]])
np.where(temp > 0, 1, 0)array([[1, 0, 1, 1], [1, 1, 1, 1], [0, 0, 1, 0], [1, 0, 1, 0]])
temp > 0array([[ True, False, True, True], [ True, True, True, True], [False, False, True, False], [ True, False, True, False]])
np.where([[ True, False, True, True],
[ True, True, True, True],
[False, False, True, False],
[ True, False, True, False]], 1, 0)array([[1, 0, 1, 1], [1, 1, 1, 1], [0, 0, 1, 0], [1, 0, 1, 0]])
temparray([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ], [-1.48252741, -0.69347186, 1.1 , -0.30606473], [ 0.39438905, -1.31770556, 1.1 , -1.52812773]])
# 判断前四个股票前四天的涨跌幅 大于0.5并且小于1的，换为1，否则为0
# (temp > 0.5) and (temp < 1)
np.logical_and(temp > 0.5, temp < 1)array([[False, False, False, False], [False, False, False, False], [False, False, False, False], [False, False, False, False]])
# 判断前四个股票前四天的涨跌幅 大于0.5或者小于-0.5的，换为1，否则为0
np.logical_or(temp > 0.5, temp < -0.5)array([[ True, False, False, False], [False, False, False, True], [ True, True, True, False], [False, True, True, True]])
np.where(np.logical_or(temp > 0.5, temp < -0.5), 11, 3)array([[11, 3, 3, 3], [ 3, 3, 3, 11], [11, 11, 11, 3], [ 3, 11, 11, 11]])
3.4、股票涨跌幅统计运算
# 前四只股票前四天的最大涨幅
temp
# shape: (4, 4) 0 1array([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ], [-1.48252741, -0.69347186, 1.1 , -0.30606473], [ 0.39438905, -1.31770556, 1.1 , -1.52812773]])
temp.max(axis=0)array([1.1 , 0.24078108, 1.1 , 1.1 ])
np.max(temp, axis=-1)array([1.1, 1.1, 1.1, 1.1])
np.argmax(temp, axis=-1)array([0, 3, 2, 2])
3.5、数组与数的运算
arr = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr / 10array([[0.1, 0.2, 0.3, 0.2, 0.1, 0.4], [0.5, 0.6, 0.1, 0.2, 0.3, 0.1]])
3.6、数组与数组的运算
arr1 = np.array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr2 = np.array([[1, 2, 3, 4], [3, 4, 5, 6]])
arr1 # (2, 6)array([[1, 2, 3, 2, 1, 4], [5, 6, 1, 2, 3, 1]])
arr2 # (2, 4)array([[1, 2, 3, 4], [3, 4, 5, 6]])
arr2 = np.array([[1], [3]])
arr2array([[1], [3]])
arr1 + arr2array([[2, 3, 4, 3, 2, 5], [8, 9, 4, 5, 6, 4]])
arr1 * arr2array([[ 1, 2, 3, 2, 1, 4], [15, 18, 3, 6, 9, 3]])
arr1 / arr2array([[1. , 2. , 3. , 2. , 1. , 4. ], [1.66666667, 2. , 0.33333333, 0.66666667, 1. , 0.33333333]])
3.7、 矩阵运算
# ndarray存储矩阵
data = np.array([[80, 86],
[82, 80],
[85, 78],
[90, 90],
[86, 82],
[82, 90],
[78, 80],
[92, 94]])# matrix存储矩阵
data_mat = np.mat([[80, 86],
[82, 80],
[85, 78],
[90, 90],
[86, 82],
[82, 90],
[78, 80],
[92, 94]])
type(data_mat)numpy.matrixlib.defmatrix.matrix
data # (8, 2) * (2, 1) = (8, 1)array([[80, 86], [82, 80], [85, 78], [90, 90], [86, 82], [82, 90], [78, 80], [92, 94]])
weights = np.array([[0.3], [0.7]])
weightsarray([[0.3], [0.7]])
weights_mat = np.mat([[0.3], [0.7]])
weights_matmatrix([[0.3], [0.7]])
np.matmul(data, weights)
data @ weights
np.dot(data, weights)
data_mat * weights_matarray([[84.2], [80.6], [80.1], [90. ], [83.2], [87.6], [79.4], [93.4]])
3.8、 合并
a = stock_change[:2, 0:4]
b = stock_change[4:6, 0:4]
aarray([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ]])
a.shape(2, 4)
a.reshape((-1, 2))array([[ 1.1 , -0.45576704], [ 0.29667843, 0.16606916], [ 0.36775845, 0.24078108], [ 0.122042 , 1.1 ]])
barray([[-0.9822216 , -1.09482936, -0.81834523, 1.1 ], [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
np.hstack((a, b))array([[ 1.1 , -0.45576704, 0.29667843, 0.16606916, -0.9822216 , -1.09482936, -0.81834523, 1.1 ], [ 0.36775845, 0.24078108, 0.122042 , 1.1 , 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
np.concatenate((a, b), axis=1)array([[ 1.1 , -0.45576704, 0.29667843, 0.16606916, -0.9822216 , -1.09482936, -0.81834523, 1.1 ], [ 0.36775845, 0.24078108, 0.122042 , 1.1 , 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
np.vstack((a, b))array([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ], [-0.9822216 , -1.09482936, -0.81834523, 1.1 ], [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
np.concatenate((a, b), axis=0)array([[ 1.1 , -0.45576704, 0.29667843, 0.16606916], [ 0.36775845, 0.24078108, 0.122042 , 1.1 ], [-0.9822216 , -1.09482936, -0.81834523, 1.1 ], [ 0.41739964, -0.26826893, -0.70003442, -0.58593912]])
3.9、 Numpy读取
data = np.genfromtxt("test.csv", delimiter=",")
dataarray([[ nan, nan, nan, nan], [ 1. , 123. , 1.4, 23. ], [ 2. , 110. , nan, 18. ], [ 3. , nan, 2.1, 19. ]])
type(data[2, 2])numpy.float64
def fill_nan_by_column_mean(t):
for i in range(t.shape[1]):
# 计算nan的个数
nan_num = np.count_nonzero(t[:, i][t[:, i] != t[:, i]])
if nan_num > 0:
now_col = t[:, i]
# 求和
now_col_not_nan = now_col[np.isnan(now_col) == False].sum()
# 和/个数
now_col_mean = now_col_not_nan / (t.shape[0] - nan_num)
# 赋值给now_col
now_col[np.isnan(now_col)] = now_col_mean
# 赋值给t，即更新t的当前列
t[:, i] = now_col
return tdataarray([[ nan, nan, nan, nan], [ 1. , 123. , 1.4, 23. ], [ 2. , 110. , nan, 18. ], [ 3. , nan, 2.1, 19. ]])
fill_nan_by_column_mean(data)array([[ 2. , 116.5 , 1.75, 20. ], [ 1. , 123. , 1.4 , 23. ], [ 2. , 110. , 1.75, 18. ], [ 3. , 116.5 , 2.1 , 19. ]])
三、Pandas
什么是Pandas-数据处理工具
便捷的数据处理能力
集成了Numpy、Matplotlib
读取文件方便
import numpy as np
# 创建一个符合正态分布的10个股票5天的涨跌幅数据
stock_change = np.random.normal(0, 1, (10, 5))
stock_changearray([[-0.07726903, 0.40607587, 1.26740233, 1.48676212, -1.35987104], [ 0.28361364, 0.43101642, -0.77154311, 0.48286211, -0.30724683], [-0.98583786, -1.96339732, 0.31658224, -1.96541561, -0.39274454], [ 2.38020637, 1.47056011, -0.45253103, -0.77381961, 0.4822656 ], [ 2.05044671, -0.0743407 , 0.10900497, 0.00982431, -0.06639766], [-1.62883603, 2.370443 , -0.14230101, -1.73515932, 1.6128039 ], [ 0.59420384, 0.09903473, -2.82975368, 0.63599429, -0.40809638], [ 1.27884397, -0.42832722, 1.07118356, -0.04453698, -0.19217219], [ 0.35350472, -0.73933626, 0.81653138, -0.40873922, 1.24391025], [-0.66201232, -0.53088568, -2.01276069, 0.03709581, 0.86862061]])
0 |
1 |
2 |
3 |
4 |
|
0 |
-0.077269 |
0.406076 |
1.267402 |
1.486762 |
-1.359871 |
1 |
0.283614 |
0.431016 |
-0.771543 |
0.482862 |
-0.307247 |
2 |
-0.985838 |
-1.963397 |
0.316582 |
-1.965416 |
-0.392745 |
3 |
2.380206 |
1.470560 |
-0.452531 |
-0.773820 |
0.482266 |
4 |
2.050447 |
-0.074341 |
0.109005 |
0.009824 |
-0.066398 |
5 |
-1.628836 |
2.370443 |
-0.142301 |
-1.735159 |
1.612804 |
6 |
0.594204 |
0.099035 |
-2.829754 |
0.635994 |
-0.408096 |
7 |
1.278844 |
-0.428327 |
1.071184 |
-0.044537 |
-0.192172 |
8 |
0.353505 |
-0.739336 |
0.816531 |
-0.408739 |
1.243910 |
9 |
-0.662012 |
-0.530886 |
-2.012761 |
0.037096 |
0.868621 |
# 添加行索引
stock = ["股票{}".format(i) for i in range(10)]
pd.DataFrame(stock_change, index=stock)0 |
1 |
2 |
3 |
4 |
|
股票0 |
-0.077269 |
0.406076 |
1.267402 |
1.486762 |
-1.359871 |
股票1 |
0.283614 |
0.431016 |
-0.771543 |
0.482862 |
-0.307247 |
股票2 |
-0.985838 |
-1.963397 |
0.316582 |
-1.965416 |
-0.392745 |
股票3 |
2.380206 |
1.470560 |
-0.452531 |
-0.773820 |
0.482266 |
股票4 |
2.050447 |
-0.074341 |
0.109005 |
0.009824 |
-0.066398 |
股票5 |
-1.628836 |
2.370443 |
-0.142301 |
-1.735159 |
1.612804 |
股票6 |
0.594204 |
0.099035 |
-2.829754 |
0.635994 |
-0.408096 |
股票7 |
1.278844 |
-0.428327 |
1.071184 |
-0.044537 |
-0.192172 |
股票8 |
0.353505 |
-0.739336 |
0.816531 |
-0.408739 |
1.243910 |
股票9 |
-0.662012 |
-0.530886 |
-2.012761 |
0.037096 |
0.868621 |
# 添加列索引
date = pd.date_range(start="20180101", periods=5, freq="B")
dateDatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05'], dtype='datetime64[ns]', freq='B')
data = pd.DataFrame(stock_change, index=stock, columns=date)
data2018-01-01 00:00:00 |
2018-01-02 00:00:00 |
2018-01-03 00:00:00 |
2018-01-04 00:00:00 |
2018-01-05 00:00:00 |
|
股票0 |
-0.077269 |
0.406076 |
1.267402 |
1.486762 |
-1.359871 |
股票1 |
0.283614 |
0.431016 |
-0.771543 |
0.482862 |
-0.307247 |
股票2 |
-0.985838 |
-1.963397 |
0.316582 |
-1.965416 |
-0.392745 |
股票3 |
2.380206 |
1.470560 |
-0.452531 |
-0.773820 |
0.482266 |
股票4 |
2.050447 |
-0.074341 |
0.109005 |
0.009824 |
-0.066398 |
股票5 |
-1.628836 |
2.370443 |
-0.142301 |
-1.735159 |
1.612804 |
股票6 |
0.594204 |
0.099035 |
-2.829754 |
0.635994 |
-0.408096 |
股票7 |
1.278844 |
-0.428327 |
1.071184 |
-0.044537 |
-0.192172 |
股票8 |
0.353505 |
-0.739336 |
0.816531 |
-0.408739 |
1.243910 |
股票9 |
-0.662012 |
-0.530886 |
-2.012761 |
0.037096 |
0.868621 |