Python中二维数组 [英] Two-dimensional arrays in Python

问题描述

我读了三个独立的城市的数据和我想保持每组的二维数组的数据，但我拿过去我的code的一部分，循环不断在写东西，从我的两市第一，因为我只有一个一维数组。我应该在哪里设置这些2-D阵列，以保持我的文件组织，我应该使用这样做有什么功能和参数？

阵列应该是（为每个城市3，54，每年数据的）3X54

编辑：所有在下面（即preCIP，TMIN，TMAX）在code初始变量将在他们超过19,000元的，我结束了在$ C平均每年有超过以后开始$ C。

 导入numpy的是NP
进口matplotlib.pyplot如PLT
进口大熊猫作为PD城市= ['Lubbock.txt'，'Erie.txt'，'Oslo.txt']
年= np.arange（1960,2014,1）
months_summer =范围（5,8,1）在范围X（0，len个地（市），1）：    数据= np.genfromtxt（市[X]，skip_header = 2，usecols =（1），DTYPE =（'S8'））
    DATA2 = np.genfromtxt（市[X]，skip_header = 2，usecols =（2,3,4））    #ONLY GET 1-D阵列时我要求的形状这种变量点之后    日期= pd.DatetimeIndex（数据[：]）
    年= dates.year
    月= dates.month
    天= dates.day
    preCIP =数据2 [：0] / 10。
    TMAX = DATA2 [：1] / 10。
    TMIN = data2的[：，2] / 10。    tmaxF =（TMAX *（9./5。））+ 32。
    tminF =（TMIN *（9./5。））+ 32。
    precipinches = preCIP * 0.03937007874    tmax_avg = []    JJA3tmax_avg = []    JJAtmax_avg = []    DJFtmax_avg = []    在多年年：
        toavg = np.where（年==年）
        tmax_avg.append（np.average（TMAX [toavg]））
        在months_summer MO：
            sumtoavg = np.where（月== MO）
            JJA3tmax_avg.append（np.average（TMAX [sumtoavg]））
        JJAtmax_avg.append（np.average（JJA3tmax_avg））
        JJA3tmax_avg = []        dec_this_year =（年==年）及（月== 12）
        jan_next_year =（年==（YR + 1））及（月== 1）
        feb_next_year =（年==（YR + 1））及（月== 2）        wintoavg = np.where（dec_this_year＆安培; jan_next_year＆安培; feb_next_year）        DJFtmax_avg.append（np.average（TMAX [wintoavg]））
    tmaxmean30 = np.average（tmax_avg [1:31]）
    JJAtmaxmean30 = np.average（JJAtmax_avg [1:31]）
    DJFtmaxmean30 = np.average（DJFtmax_avg [1:31]）＃这个是我要绘制数据
    tmax_avg_dep = tmax_avg  -  tmaxmean30
    JJAtmax_avg_dep = JJAtmax_avg  -  JJAtmaxmean30
    DJFtmax_avg_dep = DJFtmax_avg  -  DJFtmaxmean30
 

解决方案

这行：

 数据[：]
 

创建的浅的副本。你需要一个的深的复制：

 进口副本
copy.deepcopy（数据）
 

从文档：

  

浅层和深层的复制之间的差别是仅用于化合物的对象（即包含其他对象的对象，例如列表或类实例）相关

  
  

  
• 浅表副本构造一个新的复合对象，然后（尽可能）插入到它在原来找到的对象引用。

  
• 深拷贝构造一种新的化合物对象，然后，递归，插入复制到该原中发现的对象。

  

I'm reading in data for three separate cities and I want to keep each set of data in a two-dimensional array, but as I get past a part of my code, loops keep writing over things from my first two cities as I only have a one-dimensional array. Where should I set up these 2-D arrays to keep my files organized and what function and arguments should I use to do so?

Arrays should be 3X54 (3 for each city, 54 for each year of data)

EDIT: All the initial variables in the code below (i.e. precip, tmin, tmax) will have over 19,000 elements in them at the beginning which I end up averaging over each year later in the code.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

city = ['Lubbock.txt','Erie.txt','Oslo.txt']
years = np.arange(1960,2014,1)
months_summer = range(5,8,1)

for x in range(0,len(city),1):

    data = np.genfromtxt(city[x], skip_header=2, usecols=(1), dtype=('S8'))
    data2 = np.genfromtxt(city[x], skip_header=2, usecols=(2,3,4))

    #ONLY GET 1-D ARRAY WHEN I ASK FOR SHAPE OF VARIABLE AFTER THIS POINT

    dates  = pd.DatetimeIndex(data[:])
    year   = dates.year
    month  = dates.month
    day    = dates.day
    precip = data2[:,0]/10.
    tmax   = data2[:,1]/10.
    tmin   = data2[:,2]/10.

    tmaxF  = (tmax*(9./5.))+32.
    tminF  = (tmin*(9./5.))+32.
    precipinches = precip*0.03937007874

    tmax_avg = []

    JJA3tmax_avg = []

    JJAtmax_avg = []

    DJFtmax_avg = []   

    for yr in years: 
        toavg = np.where(year == yr) 
        tmax_avg.append(np.average(tmax[toavg])) 


        for mo in months_summer:
            sumtoavg = np.where(month == mo)
            JJA3tmax_avg.append(np.average(tmax[sumtoavg]))


        JJAtmax_avg.append(np.average(JJA3tmax_avg))
        JJA3tmax_avg = []

        dec_this_year = (year == yr) & (month == 12)
        jan_next_year = (year == (yr+1)) & (month == 1)
        feb_next_year = (year == (yr+1)) & (month == 2)

        wintoavg = np.where(dec_this_year & jan_next_year & feb_next_year)

        DJFtmax_avg.append(np.average(tmax[wintoavg]))


    tmaxmean30 = np.average(tmax_avg[1:31])
    JJAtmaxmean30 = np.average(JJAtmax_avg[1:31])
    DJFtmaxmean30 = np.average(DJFtmax_avg[1:31])

#THIS IS THE DATA THAT I'M PLOTTING
    tmax_avg_dep = tmax_avg - tmaxmean30
    JJAtmax_avg_dep = JJAtmax_avg - JJAtmaxmean30
    DJFtmax_avg_dep = DJFtmax_avg - DJFtmaxmean30

解决方案

This line:

data[:]

creates a shallow copy. You need a deep copy:

import copy
copy.deepcopy(data)

From the docs:

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

• A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.
• A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

这篇关于Python中二维数组的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！