# 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...
from pyspark.sql import SparkSession import pyspark.pandas as ps spark = SparkSession.builder.appName('testpyspark').getOrCreate() ps_data = ps.read_csv(data_file, names=header_name) 运行apply函数,记录耗时: for col in ps_data.columns: ps_data[col] = ps_data[col].apply(apply_md5) ...
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)...
print('均值:%.3f' %data['beta'].mean()) print('标准差:%.3f' %data['beta'].std()) print('偏度:%.3f' %data['beta'].skew()) print('峰度:%.3f' %data['beta'].kurt()) y = list(range(200)) plt.figure(figsize=(16,8)) plt.hist(data['beta'], bins=100) plt.plot(len(...
rms = np.sqrt(np.mean(np.square(sample_data, dtype=np.float64))) dbfs =20.0* np.log10(max(1e-16, rms))returndbfsdefvolumeAument1(wav, dB):""" :param wav: 语音 :param dB: 音量 :return:返回以指定dB增益后的语音 """power = np.mean(wav **2)# 平均功率scalar = np.sqrt(10*...
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 = pd.get_dummies(data, columns=['city', 'type'], drop_first=True) 1. 归一化数值数据,可以使用 scikit-learn 中的 StandardScaler: from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data[['area', 'rooms']] = scaler.fit_transform(data[['area', 'rooms']]) ...
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。
from #Generic function for making a classification model and def classification_model(model, data, predictors, outcome): #Fit the model: model.fit(data[predictors],data[outcome]) on training set: predictions = model.predict(data[predictors]) ...
Unpivot a DataFrame from wide to long format, optionally leavingidentifiers set.pivot : Create a spreadsheet-style pivot table as a DataFrame.DataFrame.pivot : Pivot without aggregation that can handlenon-numeric data.DataFrame.pivot_table : Generalization of pivot that can handleduplicate values for...