# Import dataset midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") # Prepare Data # Create as many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors =[plt.cm.tab10(i/float(len(categories...
plt.rcParams['font.sans-serif'] = ['SimHei']# 用来正常显示中文标签plt.rcParams['axes.unicode_minus'] =False# 用来正常显示符号fs =16000wav_data, _ = librosa.load("/home/gxli/lgx/Data/gather_crop/clean1/2148_farend.wav", sr=fs, mono=True)# ### 画图plt.subplot(2,2,1) plt.titl...
from pyspark.ml.classification import RandomForestClassifier rf = RandomForestClassifier(labelCol="label", \ featuresCol="features", \ numTrees = 100, \ maxDepth = 4, \ maxBins = 32) # Train model with Training Data rfModel = rf.fit(trainingData) predictions = rfModel.transform(testData)...
'Utah':5000} states = ['California','Ohio','Oregon','Texax'] obj3 = pd.Series(sdata) print(obj3) obj4 = pd.Series(sdata,index = states) # 将有索引的赋值,否则为空 print(obj4) pd.isnull(obj4) # 为空的 为True
(x, y, bins=nbins, cmap=plt.cm.BuGn_r) # 高斯kde k = kde.gaussian_kde(data.T) xi, yi = np.mgrid[x.min():x.max():nbins * 1j, y.min():y.max():nbins * 1j] zi = k(np.vstack([xi.flatten(), yi.flatten()])) # 密度图 axes[3].set_title('Calculate Gaussian KDE')...
from sqlalchemy import create_engine # 创建数据库引擎engine = create_engine('oracle://user:password@ip_address:1521/orcl') # 连接数据库con = engine.connect() 如果你有数据库账号拥有创建表的权限,那么就可以对数据库进行增删改查的操作。
print("抓取的数据:", data) 2. 数据清洗与处理 使用pandas库对抓取的数据进行清洗和处理。 python 复制代码 import pandas as pd # 转换为DataFrame df = pd.DataFrame(data, columns=['Title']) # 去除重复数据 df.drop_duplicates(inplace=True) ...
data = [1, 3, 5, 2, 1, 4, 3, 5, 4, 3, 2, 1, 3, 4, 2] histogram(data) 上述代码中,我们使用`matplotlib.pyplot`库来生成直方图。`histogram`函数接受数据列表和可选的`bins`参数(默认为10),用于指定直方图的柱子数量。 在测试部分,我们创建一个包含一组数据的列表`data`,然后调用`histogram...
array(data: 'Sequence[object] | AnyArrayLike', dtype: 'Dtype | None' = None, copy: 'bool' = True) -> 'ExtensionArray' Create an array. Parameters --- data : Sequence of objectsThe scalars inside `data` should be instances of thescalar type for `dtype`. It's expected that `data...
import seaborn as snsimport matplotlib.pyplot as plt# 加载数据df = sns.load_dataset('iris', data_home='seaborn-data', cache=True)# 绘图显示sns.kdeplot(df['sepal_width'])plt.show() 使用Seaborn的kdeplot()进行绘制,结果如下。03.直方图 直方图,可视化一组或多组数据的分布情况。