今天博士師兄讓我?guī)兔?shí)現(xiàn)一個(gè)畫(huà)圖的代碼，雖然研究背景比較專(zhuān)業(yè)，但是需求就是在某兩個(gè)大表中找到相同的數(shù)據(jù)并畫(huà)柱狀圖，下面就直接貼代碼了，主要用的就是numpy包，注釋也比較詳細(xì)：

#!/usr/bin/env pythonimport numpy as np import xlsxwriter# Step 1: Read data from flux plane raw particle files def caculate():# Change the file names to match your project settingsf1 = 'FLUX_upstream_raw_particle'f2 = 'FLUX_downstream_raw_particle'# Check column numbers, because they might be different for your project# Fluxplane## @ 1 "Time" "s"# @ 2 "Unique particle ID" ""tCol = 1pidCol = 2t1, pid1 = np.genfromtxt(f1, usecols=(tCol - 1, pidCol - 1), unpack=True)t2, pid2 = np.genfromtxt(f2, usecols=(tCol - 1, pidCol - 1), unpack=True)# Step 2: Calculate travel time between the two flux planes# 用numpy包創(chuàng)建一個(gè)array數(shù)組travelTimeArray = np.array([])# 將第一個(gè)文件里的第一列和第二列的每?jī)蓚€(gè)元素組裝成一個(gè)元組 比如第一個(gè)文件的第一行是1,3；第二行是2,9# 現(xiàn)在的zip(t1, pid1)就變成了[(1,3),(2,9)]for myTime, myPID in zip(t1, pid1):# 判斷第一個(gè)表格的Id是否在第二個(gè)文件的id列里出現(xiàn)if np.isin(myPID, pid2):# 遍歷出第二個(gè)文件里面的第二列的id和第一個(gè)文件里的id相等的一個(gè)array數(shù)組，每一個(gè)# arrayIndex就是第二個(gè)文件里相應(yīng)的pid的行數(shù)for arrayIndex in np.where(pid2 == myPID)[0]:# if t2[arrayIndex] > myTime:# 把得到的數(shù)據(jù)保存到之前創(chuàng)建的travelTimeArray數(shù)組中travelTimeArray = np.append(travelTimeArray, t2[arrayIndex] - myTime)# 跳出內(nèi)層循環(huán)break# --------------------------保存到excel------------------------------------------#先定義一個(gè)workbook就是excel表格，參數(shù)填寫(xiě)想要的名稱，每次執(zhí)行前一定要保證沒(méi)有此文件workbook = xlsxwriter.Workbook('hello.xlsx') # 建立文件# 添加sheet的名字worksheet = workbook.add_worksheet("time")# 遍歷剛才保存的數(shù)組，遍歷上限是數(shù)組的長(zhǎng)度，也就是結(jié)果的個(gè)數(shù)for i in range(len(travelTimeArray)):# 向excel中寫(xiě)入數(shù)據(jù)，i+1的原因是excel里的第一行是1開(kāi)始，但是range函數(shù)是從0開(kāi)始worksheet.write('A' + str(i + 1), travelTimeArray[i]) # 向A1寫(xiě)入# 執(zhí)行完后關(guān)閉表格IO操作workbook.close()# Step 3: Print some general information about the calculated travel timesprint("========= General information about calculated travel times =========")print("Length of travelTimeArray =", len(travelTimeArray))print("Average travel time =", "{0:.4f}".format(np.mean(travelTimeArray)), "s")print("Standard dev. of travel time =", "{0:.4f}".format(np.std(travelTimeArray)), "s")print("Minimum travel time =", "{0:.4f}".format(np.amin(travelTimeArray)), "s")print("Maximum travel time =", "{0:.4f}".format(np.amax(travelTimeArray)), "s")# Step 4: Calculate a histogram of the travel times, to summarize the datahist, bin_edges = np.histogram(travelTimeArray)print()print("========= Histogram of calculated travel times =========")print("bin edges:", bin_edges)print("histogram:", hist)if __name__ == '__main__':caculate()

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