import pandas as pd inputfile1 = 'data/GoodsOrder.csv' inputfile2 = 'data/GoodsTypes.csv' # 读入数据 data = pd.read_csv(inputfile1,encoding = 'gbk') types = pd.read_csv(inputfile2,encoding = 'gbk') group = data.groupby(['Goods']).count().reset_index() group_sorted = group.sort_values('id',ascending = False).reset_index() # 总量 data_nums = data.shape[0] del group_sorted['index'] group_sorted.head()标记注释

该段代码的作用是：读取名为"GoodsOrder.csv"和"GoodsTypes.csv"的数据文件，并对商品销量进行统计和排名。其中，使用了pandas库的read_csv函数读取数据文件，使用groupby函数对商品进行分类汇总，使用count函数统计每个商品的销量，使用sort_values函数对结果按照销量进行排序，使用reset_index函数重新设置索引，并计算了数据的总记录数。最后，使用del函数删除了结果中的index列，并使用head函数展示了结果的前5行。

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import pandas as pd import numpy as np from sklearn.cluster import DBSCAN from sklearn import metrics from sklearn.cluster import KMeans import os def dbscan(input_file): ## 纬度在前，经度在后 [latitude, longitude] columns = ['lat', 'lon'] in_df = pd.read_csv(input_file, sep=',', header=None, names=columns) # represent GPS points as (lat, lon) coords = in_df.as_matrix(columns=['lat', 'lon']) # earth's radius in km kms_per_radian = 6371.0086 # define epsilon as 0.5 kilometers, converted to radians for use by haversine # This uses the 'haversine' formula to calculate the great-circle distance between two points # that is, the shortest distance over the earth's surface # http://www.movable-type.co.uk/scripts/latlong.html epsilon = 0.5 / kms_per_radian # radians() Convert angles from degrees to radians db = DBSCAN(eps=epsilon, min_samples=15, algorithm='ball_tree', metric='haversine').fit(np.radians(coords)) cluster_labels = db.labels_ # get the number of clusters (ignore noisy samples which are given the label -1) num_clusters = len(set(cluster_labels) - set([-1])) print('Clustered ' + str(len(in_df)) + ' points to ' + str(num_clusters) + ' clusters') # turn the clusters in to a pandas series # clusters = pd.Series([coords[cluster_labels == n] for n in range(num_clusters)]) # print(clusters) kmeans = KMeans(n_clusters=1, n_init=1, max_iter=20, random_state=20) for n in range(num_clusters): # print('Cluster ', n, ' all samples:') one_cluster = coords[cluster_labels == n] # print(one_cluster[:1]) # clist = one_cluster.tolist() # print(clist[0]) kk = kmeans.fit(one_cluster) print(kk.cluster_centers_) def main(): path = './datas' filelist = os.listdir(path) for f in filelist: datafile = os.path.join(path, f) print(datafile) dbscan(datafile) if __name__ == '__main__': main()

这是一个 Python 代码，主要使用了 Pandas、NumPy、sklearn.cluster 等库实现了 DBSCAN 和 KMeans 聚类算法。代码读入了一个文件夹中的多个文件，每个文件都是 GPS 坐标点的经纬度信息，然后使用 DBSCAN 算法进行聚类，并且输出了聚类的个数。接着使用 KMeans 算法对每个聚类进行细分，最后输出每个聚类的中心点坐标。

Read the planetary data in the text file S12_planet.txt into a Pandas DataFrame. The file will be given in wechat group. Task1, calculate the density of each planet relative to that of the Earth (i.e., the Earth’s density is 1) and add the results as a “density” column in the DataFrame. Print out the DataFrame sorted from the largest to smallest based on the planet diameter. The density formula is � � � � � � � = � / � = 3 � / ( 4 � ∗ � 3 ) density=M/V=3M/(4π∗r 3 ) Task2, read the 2 items in input as "A" and "Standard". A is a "String" and Standard is a "float". Create a Pandas Series that have "A" greater than "Standard", sorted from most to least based on the planet's "A". For example, if input is "year 500", then collect the data of planets whose years are greater than 500. Input A string and a float. Output Task1: DataFrame sorted on diameter Task2: Panda Series sorted on A. Note: You dont need to deal with rounding format. Just let the output as default like that in example.

Task1:

import pandas as pd
import numpy as np

# Read the planetary data into a DataFrame
df = pd.read_csv('S12_planet.txt', delimiter='\t')

# Calculate the density of each planet relative to that of the Earth
earth_density = 1
df['density'] = (3 * df['mass']) / (4 * np.pi * (df['diameter'] ** 3)) / earth_density

# Sort the DataFrame based on planet diameter
sorted_df = df.sort_values(by='diameter')

# Print the sorted DataFrame
print(sorted_df)

Task2:

import pandas as pd

# Read the input values
A = input('Enter string A: ')
Standard = float(input('Enter float Standard: '))

# Create a Pandas Series with planets whose A is greater than Standard
filtered_series = df.loc[df['A'] > Standard, 'A'].sort_values(ascending=False)

# Print the filtered Series
print(filtered_series)

注意：以上代码中的 `df` 表示在 Task1 中读入的 DataFrame。如果需要在 Task2 中使用，需要先在代码中进行读取。