一些常用的python代码合集，方便检索引用

模块1：读写excel文件

from datetime import datetime import odps import xlwt import os from odps import DataFrame import pandas as pd import xlrd import numpy as np from collections import defaultdict from collections import Counter # 写入工作簿 def write_imf(fl_save_path, data): wb = xlwt.Workbook(encoding='utf-8') # 不写encoding会出现编码错误 sh = wb.add_sheet(u'data', cell_overwrite_ok=True) # 表头部分,单独写 colnames = data.columns.values for i in range(0, data.shape[1]): sh.write(0, i, colnames[i]) # 表内容,循环写入,好像没简便的方法 for i in range(1, len(data) + 1): for j in range(0, data.shape[1]): value = data.iloc[i - 1, j] # print(value) # 这里的坑特别多！！！数据读进来之后就成numpy.xxx64的类型了，在dataframe的时候就需要统一干掉！ try: value.dtype if value.dtype == 'int64': value = int(value) # print('value is:%d,type is:%s'%(value,type(value))) if value.dtype == 'float64': value = float(value) # print('value is:%d,type is:%s' % (value, type(value))) except(RuntimeError, TypeError, NameError, ValueError, AttributeError): pass sh.write(i, j, value) wb.save(fl_save_path) print('congratulation save successful!') def save_pd_to_csv(fl_save_path, data): try: # 直接转csv不加encoding，中文会乱码 data.to_csv(fl_save_path, encoding="utf_8_sig", header=True, index=False) # 存储 return True except: return False def get_excel_content(file_path): # 获取excel内的SQL语句，需要通过xlrd获取workbook中的SQL内容，或者读txt，后续改为配置文件 wb = xlrd.open_workbook(file_path, encoding_override='utf-8') sht = wb.sheet_by_index(0) # 默认第一个工作表 # print(sht.name) wb_cont_imf = [] nrows = sht.nrows # 行数 wb_cont_imf = [sht.row_values(i) for i in range(0, nrows)] # 第一个工作表内容按行循环写入 df = pd.DataFrame(wb_cont_imf[1:], columns=wb_cont_imf[0]) return df

模块2：获取各种时间

# 获取年月第一天最后一天 def getMonthFirstDayAndLastDay(year=None, month=None): """ :param year: 年份，默认是本年，可传int或str类型 :param month: 月份，默认是本月，可传int或str类型 :return: firstDay: 当月的第一天，datetime.date类型 lastDay: 当月的最后一天，datetime.date类型 """ if year: year = int(year) else: year = datetime.date.today().year if month: month = int(month) else: month = datetime.date.today().month # 获取当月第一天的星期和当月的总天数 firstDayWeekDay, monthRange = calendar.monthrange(year, month) # 获取当月的第一天 firstDay = datetime.date(year=year, month=month, day=1) lastDay = datetime.date(year=year, month=month, day=monthRange) # return firstDay, lastDay return lastDay

模块3：pd中的dataframe转png

# dataframe2png def render_mpl_table(data, col_width=5.0, row_height=0.625, font_size=1, header_color='#40466e', row_colors=['#f1f1f2', 'w'], edge_color='w', bbox=[0, 0, 1, 1], header_columns=0, ax=None,**kwargs): if ax is None: # size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height]) # fig, ax = plt.subplots(figsize=size) fig, ax = plt.subplots() # 创建一个空的绘图区 # 衍生知识点，服务器上安装中文字体 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 # plt.rcParams['font.sans-serif'] = ['WenQuanYi Zen Hei Mono'] plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.style.use('ggplot') ax.axis('off') mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs) mpl_table.auto_set_font_size(False) mpl_table.set_fontsize(font_size) for k, cell in six.iteritems(mpl_table._cells): cell.set_edgecolor(edge_color) nrow = k[0] ncol = k[1] # 设置表格底色 if nrow == 0 or ncol < header_columns: cell.set_text_props(weight='bold', color='w') cell.set_facecolor(header_color) else: cell.set_facecolor(row_colors[k[0] % len(row_colors)]) # # 对当日异常数据为0的部分，着重体现 # row_num = [] # for k, cell in mpl_table._cells.items(): # nrow = k[0] # ncol = k[1] # val = cell.get_text().get_text() # if nrow > 0 and ncol == 2 and val != '0': # row_num.append(nrow) # for k, cell in six.iteritems(mpl_table._cells): # nrow = k[0] # # 设置表格底色 # if nrow in row_num: # cell.set_facecolor('gold') # 保留原图的设置 # fig.set_size_inches(width/100.0,height/100.0)#输出width*height像素 plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0) plt.margins(0, 0) return ax

模块4：绘制词云

#!/user/bin/python # -*- coding:utf-8 -*- _author_ = 'xisuo' import datetime import calendar import xlwt import os import pandas as pd import xlrd import openpyxl import numpy as np from collections import defaultdict import platform from wordcloud import WordCloud,STOPWORDS import matplotlib.pyplot as plt from PIL import Image def create_wordcloud(docs=None,imgs=None,filename=None): ''' :param docs:读入词汇txt，尽量不重复 :param imgs: 读入想要生成的图形，网上随便找 :param filename: 保存图片文件名 :return: ''' # Read the whole text. text = open(os.path.join(current_file, docs)).read() alice_mask = np.array(Image.open(os.path.join(current_file, imgs))) print(font_path) wc = WordCloud(background_color="white", max_words=2000, font_path=font_path, # 设置字体格式，如不设置显示不了中文 mask=alice_mask, stopwords=STOPWORDS.add("said") ) # generate word cloud wc.generate(text) # store to file if filename is None:filename="词云结果.png" wc.to_file(os.path.join(current_file, filename)) def main(): docs='demo.txt' #读入的文本 imgs="eg.jpg" #需要绘制的图像 filename='res_eg.png' #保存图片文件名 create_wordcloud(docs=docs,imgs=imgs,filename=filename) print('create wordcloud successful') if __name__ == '__main__': start_time = datetime.datetime.now() print('start running program at:%s' % start_time) systemp_type = platform.system() if (systemp_type == 'Windows'): plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 font_path='simfang.ttf' try: current_path = os.getcwd() except: current_path = os.path.dirname(__file__) current_file = os.path.join(current_path, 'docs') current_file = current_path elif (systemp_type == 'Linux'): font_path = 'Arial Unicode MS.ttf' plt.rcParams['font.family'] = ['Arial Unicode MS'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 current_file = '/home/xisuo/mhc_work/docs/' # 服务器上的路径 else: quit() if not os.path.exists(current_file): os.mkdir(current_file) print('目录中部存在docs文件夹，完成新文件夹创建过程。') print('当前操作系统：%s，文件存储路径为：%s' % (systemp_type, current_file)) main() end_time = datetime.datetime.now() tt = end_time - start_timepython print('ending time:%s', end_time) print('this analysis total spend time:%s' % tt.seconds)

模块5：下载ppt素材

#!/user/bin/python #-*- coding:utf-8 -*- _author_ = 'xisuo' import urllib.request import requests from bs4 import BeautifulSoup from lxml import etree import os url='http://www.pptschool.com/1491.html' response=requests.get(url).text # soup=BeautifulSoup(response,'lxml') # cont=soup.find('article', class_='article-content') html=etree.HTML(response) src_list=html.xpath('//div/article/p/img/@src') current_path=os.path.dirname(__file__) save_path=os.path.join(current_path,'ppt_img') if os.path.exists(save_path): pass else: os.mkdir(save_path) print('img folder create successful') i=1 for src in src_list: save_img_path=os.path.join(save_path,'%d.jpg'%i) try: with open(save_img_path,'wb') as f: f.write(urllib.request.urlopen(src).read()) f.close() i=i+1 print('save true') except Exception as e: print('save img fail')

模块6：模型存储和读取

rom sklearn import joblib from sklearn import svm from sklearn2pmml import PMMLPipeline, sklearn2pmml import pickle def save_model(train_X,train_y): '''' save model :return: ''' clf = svm.SVC() clf.fit(X, y) joblib.dump(clf, "train_model.m") sklearn2pmml(clf, "train_model.pmml") with open('train_model.pickle', 'wb') as f: pickle.dump(clf, f) return True def load_model(): ''' laod model :return: ''' clf_joblib=joblib.load('train_model.m') clf_pickle== pickle.load(open('linearregression.pickle','rb')) return clf_joblib,clf_pickle

模块7：TF-IDF

import time import pandas as pd import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer # 读取数据 - 性能不好待优化 print('开始读取KeyTag标签...') read_data_path = 'D:/untitled/incomelevel_kwtag_20190801.txt' load_data = pd.read_csv(read_data_path, sep='t',encoding='utf-8') data = pd.DataFrame(load_data,columns = ['income_level','kw_tag']) print('...读取KeyTag标签完成') # 将数据分组处理 print('开始分组处理KeyTag标签...') # 高收入 incomelevel_top = data[data['income_level'] == '高'] incomelevel_top = incomelevel_top.head() #test kw_tag_top = ' '.join(incomelevel_top['kw_tag']) print('kw_tag_top : n',kw_tag_top) # 中收入 incomelevel_mid = data[data['income_level'] == '中'] incomelevel_mid = incomelevel_mid.head() #test kw_tag_mid = ' '.join(incomelevel_mid['kw_tag']) print('kw_tag_mid : n',kw_tag_mid) # 低收入 incomelevel_low = data[data['income_level'] == '低'] incomelevel_low = incomelevel_low.head() #test kw_tag_low = ' '.join(incomelevel_low['kw_tag']) print('kw_tag_low : n',kw_tag_low) print('...分组处理KeyTag标签完成') # 开始加载TF-IDF vectorizer = CountVectorizer() result = vectorizer.fit_transform([kw_tag_top, kw_tag_mid, kw_tag_low]) transformer = TfidfVectorizer() kw_tag_score = transformer.fit_transform([kw_tag_top, kw_tag_mid, kw_tag_low]) print('...KeyTag分词结束') # 获取全量标签 kw_tag_value = transformer.get_feature_names() result_target = pd.DataFrame(kw_tag_value,columns = ['kw_tag']) print('result_target : n',result_target) # 分词得分处理 tf_score = kw_tag_score.toarray() print('tf_score : n',tf_score) kw_tag_score_top = pd.DataFrame(tf_score[0],columns = ['kw_tag_score_top']) # 217 kw_tag_score_mid = pd.DataFrame(tf_score[1],columns = ['kw_tag_score_mid']) kw_tag_score_low = pd.DataFrame(tf_score[2],columns = ['kw_tag_score_low']) print(len(kw_tag_score_top))

模块8：生成省市地图

import time import pandas as pd import xlrd import re import matplotlib.pyplot as plt import six import numpy as np # 载入ppt和pyecharts相关的包 from pyecharts.render import make_snapshot from snapshot_phantomjs import snapshot from pyecharts import options as opts from collections import defaultdict from pyecharts.charts import Bar, Geo, Map, Line,Funnel,Page import os from example.commons import Faker def create_zjs_map(): folder_path = os.getcwd() file_name = "白皮书数据地图.xlsx" file_path = os.path.join(folder_path, file_name) dat = get_excel_content(file_path, sheet_name="省份地图") df = dat[['城市', '渗透率']] df.columns = ['city', 'penarate'] print(df) # df['city'] = df['city'].apply(lambda x: reg.sub('', x)) citys = df['city'].values.tolist() values = df['penarate'].values.tolist() print(citys) print('{:.0f}%'.format(max(values)*100),'{:.0f}%'.format(min(values)*100)) city_name='浙江' penetration_map = ( Map(init_opts=opts.InitOpts(width='1200px', height='1000px', bg_color='white')) .add("{}透率分布".format(city_name), [list(z) for z in zip(citys, values)], city_name) .set_series_opts( label_opts=opts.LabelOpts( is_show=True, font_size=15 ) ) .set_global_opts( visualmap_opts=opts.VisualMapOpts( is_show=True, max_=max(values), min_=min(values), is_calculable=False, orient='horizontal', split_number=3, range_color=['#C2D5F8', '#88B0FB', '#4D8AFD'], range_text=['{:.0f}%'.format(max(values)*100),'{:.0f}%'.format(min(values)*100)], pos_left='10%', pos_bottom='15%' ), legend_opts=opts.LegendOpts(is_show=False) ) ) # penetration_map.render() make_snapshot(snapshot, penetration_map.render(), "zj_map.png") print('保存 zj_map.png') return penetration_map def create_county_map(city_name): folder_path = os.getcwd() file_name = "白皮书数据地图.xlsx" file_path = os.path.join(folder_path, file_name) dat = get_excel_content(file_path, sheet_name="城市地图") df = dat[['city', 'county', 'penarate']][dat.city == city_name] citys = df['county'].values.tolist() values = df['penarate'].values.tolist() max_insurance = max(values) print(citys) province_penetration_map = ( Map(init_opts=opts.InitOpts(width='1200px', height='1000px', bg_color='white')) .add("{}透率分布".format(city_name), [list(z) for z in zip(citys, values)], reg.sub('',city_name)) .set_series_opts( label_opts=opts.LabelOpts( is_show=True, font_size=15 ) ) .set_global_opts( visualmap_opts=opts.VisualMapOpts( is_show=True, max_=max(values), min_=min(values), is_calculable=False, orient='horizontal', split_number=3, range_color=['#C2D5F8', '#88B0FB', '#4D8AFD'], range_text=['{:.0f}%'.format(max(values) * 100), '{:.0f}%'.format(min(values) * 100)], pos_left='10%', pos_bottom='5%' ), legend_opts=opts.LegendOpts(is_show=False) ) ) # insurance_map.render() make_snapshot(snapshot, province_penetration_map.render(), "city_map_{}.png".format(city_name)) print('保存 city_map_{}.png'.format(city_name)) return province_penetration_map def create_funnel_label(): folder_path=os.getcwd() file_name = "白皮书数据地图.xlsx" file_path = os.path.join(folder_path, file_name) dat = get_excel_content(file_path, sheet_name="漏斗图") df = dat[['category', 'cnt']] print(df) category = df['category'].values.tolist() values = df['cnt'].values.tolist() funnel_map = ( Funnel(init_opts=opts.InitOpts(width='1200px', height='1000px', bg_color='white')) .add("漏斗图", [list(z) for z in zip(category, values)]) .set_series_opts( label_opts=opts.LabelOpts( position='inside', font_size=16, ) ) .set_global_opts( legend_opts=opts.LegendOpts(is_show=False) ) ) # insurance_map.render() make_snapshot(snapshot, funnel_map.render(), "funnel.png") print('保存 funnel.png') return funnel_map city_list=['温州市','杭州市','绍兴市','嘉兴市','湖州市','宁波市','金华市','台州市','衢州市','丽水市','舟山市'] for city_name in city_list: create_county_map(city_name)