This code removes outliers using Z-score and normalizes the data between 0 and 1. Create 3D plot To create a 3D scatter plot, you can use matplotlib: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111,...
sns.histplot(df['price'], kde=True) plt.subplot(1,2,2) sns.boxplot(y='price', x='category', data=df) 3.2 地理数据可视化 使用Geopandas处理空间数据: import geopandas as gpd world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) ax = world.plot(figsize=(15,10), ...
filename=r'E:\aaaa\world_geo.nc'f=xr.open_dataset(filename)lat=f['y'][3591:3621]height=f['z'][3591:3621,8669]fig=plt.figure(figsize=(4,1.5),dpi=700)ax=fig.add_axes([0,0,1,1])ax.plot(lat,height,c='k',lw=1)ax.fill_between(lat,height,facecolor='white',hatch='///')#...
plt.rcParams['font.sans-serif']=['SimHei']#用来正常显示标签中文plt.rcParams['axes.unicode_minus']=False#用来正常显示负号plt.figure()#建立图像p = data.boxplot(return_type='dict')#画箱型图,直接使用DataFrame的方法x = p['fliers'][0].get_xdata()#‘flies’为异常值的标签y = p['fliers'...
data = gpd.read_file('https://geo.datav.aliyun.com/areas_v3/bound/510000_full.json').to_crs('EPSG:4573') data.head() 简单分级统计 以下代码通过scheme分级统计四川省各地级市所包含区县数。 ax = data.plot( column="childrenNum",
from_file("GIS_CENSUS_poly.shp") census.plot() plot.show() Spectral Python python的光谱功能包,是一个专门处理遥感波段数据的高级光能包,适用于高光谱处理方面的应用。 总结 这篇文章是python地理空间分析的一个开头,简单介绍了地理空间分析对于数据分析和气象的重要作用,介绍了地理空间分析的对象,常用到的...
savefig("H:/plot_save/picture/test12.jpg", bbox_inches='tight', dpi=600) plt.show() 特殊的阵列 利用plt.subplot()逐一添加子图的方法, 还可以实现更加复杂的排版形式, 例如如下程序: import numpy as np import matplotlib.pyplot as plt ## Load the data from data file #x = np.linspace(...
plt.plot(df ['Mes'],df ['deep learning'],label ='deep learning')结果如下:每种颜色代表哪个变量还不是很清楚。我们将通过添加图例和标题来改进图表。 plt.plot(df['Mes'], df['data science'], label='data science')plt.plot(df['Mes'], df['machine learning'], label='machine learning')...
graph_objects as go import numpy as np # Generate sample data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) x, y = np.meshgrid(x, y) z = np.sin(np.sqrt(x**2 + y**2)) # Create a 3D surface plot fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)...
def load_data(filename): df = pd.read_csv(filename, sep=",", index_col=False) df.columns = ["housesize", "rooms", "price"] data = np.array(df, dtype=float) plot_data(data[:,:2], data[:, -1]) normalize(data) return data[:,:2], data[:, -1] 我们稍后将调用上述函数来...