This article introduces the realization of two lists of python calculated correlation coefficients, the paper sample code described in great detail, has a certain reference value of learning for all of us to learn or work, we need friends to below as the small series together learn from it
calculate the correlation coefficient with pandas
Calculating the correlation coefficient with the pandas, for example, I want to know the wind speed and wind direction disorders (measured by standard deviation) correlation coefficient between, Here is the code:
import pandas as pd
import pylab as plt
#每小时的阵风风速平均值
all_gust_spd_mean_list = [8.21529411764706, 7.872941176470587, 7.829411764705882, 8.354117647058825, 9.025882352941174, 9.384523809523811, 9.57294117647059, 9.274117647058821, 9.050588235294118, 9.314117647058827, 8.924705882352939, 9.25176470588235, 8.978823529411764, 8.39176470588235, 7.715294117647061, 7.477647058823529, 7.272941176470586, 7.38470588235294, 7.396470588235295, 7.97261904761905, 7.716666666666666, 7.7809523809523835, 7.816666666666668, 7.897590361445783, 8.200000000000001, 8.04761904761905, 7.474999999999999, 9.855952380952383, 11.120000000000001, 10.979761904761906, 10.922619047619051, 10.841176470588234, 9.31566265060241, 8.867058823529415, 9.068235294117642, 8.774698795180722, 8.629411764705884, 8.292941176470586, 7.640000000000007, 7.422352941176469, 7.464705882352944, 8.210588235294113, 8.558823529411763, 8.93095238095238, 9.001176470588234, 8.538095238095238, 8.965882352941172, 9.855294117647057, 8.318918918918921, 9.217647058823525, 8.86470588235294, 8.840000000000002, 9.44235294117647, 9.352380952380953, 9.307058823529408, 9.64047619047619, 9.408333333333333, 9.585882352941175, 8.901190476190477, 7.698823529411764, 7.988235294117645, 9.091764705882353, 9.294117647058819, 8.996470588235297, 9.63764705882353, 9.091764705882353, 8.937647058823533, 8.838823529411764, 8.637647058823534, 8.46, 8.374117647058824, 8.24117647058823, 8.245238095238093, 8.365882352941174, 8.50235294117647, 8.291764705882352, 8.088235294117647, 7.889411764705883, 7.594117647058826, 7.216470588235293, 7.097647058823533, 7.305882352941181, 7.489411764705882, 6.815294117647058, 7.971428571428569, 7.424705882352936, 6.910588235294117, 6.071764705882354, 7.44117647058823, 7.667857142857143, 7.881176470588237, 7.929411764705881, 8.12142857142857, 8.822352941176472, 9.083529411764703, 9.028235294117646, 9.310714285714285, 9.035294117647057, 8.450588235294116, 8.414285714285713, 7.311764705882355, 6.840000000000001, 7.238095238095239, 6.641176470588236, 6.8047619047619055, 6.58705882352941, 6.826190476190474, 6.568235294117643, 7.060000000000001, 7.686904761904761, 8.348235294117643, 8.503529411764701, 8.287058823529414, 8.354117647058823, 7.624705882352941, 7.286904761904765, 7.361176470588235, 7.477647058823531, 7.343529411764706]
#每小时的阵风风向标准差
all_gust_agl_dev_list = [0.7507438242046189, 0.768823513771462, 0.849877567310481, 0.8413581558472801, 0.8571319461950748, 0.8665002025305942, 0.9053739533298005, 0.8866979720735791, 0.8045677876888446, 0.873463882661469, 0.832383480871403, 0.778659970340069, 0.7357031045047981, 0.7974723911258534, 0.8039727543149432, 0.8709723763624072, 0.8727745464337923, 0.7896422160341138, 0.8165093346129041, 0.8821296270775546, 0.9193591477905156, 0.8546566314487358, 0.8595040204296921, 0.8075641299052398, 0.7996745617071098, 0.7930869411601498, 0.7578880032016914, 0.9107571156507569, 0.8461201382346486, 0.7553646348127085, 0.8510861123303187, 0.7282631202385544, 0.8588017730198183, 0.7923449370076744, 0.8265083209111689, 0.9599970229643688, 0.8195276021290412, 0.7882592259148272, 0.8036464793287409, 0.8237184691421926, 0.8846862360656914, 0.8136869244513337, 0.8516383375155133, 0.7760301715652644, 0.8644231334629017, 0.831330440569484, 0.8061342111854616, 0.7345896810176235, 1.205089147978776, 0.8266315966774649, 0.8137345300107962, 0.8186966603954983, 0.7836182115343135, 0.8406438908681332, 0.7717723331806998, 0.7932664155269176, 0.7266183593077442, 0.719063143819583, 0.8846434855533486, 0.817552510948495, 0.7571575934024827, 0.865326265251608, 0.9099784335052563, 0.8591794583996128, 0.9295389095340467, 0.8787300860744375, 0.8724277968300532, 0.95284132003256, 0.9288772059881606, 0.8690944948691984, 0.8327213470469693, 0.8339075062700629, 0.886835675339985, 0.8439137877550847, 0.7985495396895048, 0.8406267016063169, 0.8477871130878305, 0.8844025576348077, 0.9186363354492758, 0.8888539157167654, 0.9079462071375304, 0.8699806402308554, 0.8531937701209343, 0.8833108936555343, 0.9317958602705915, 0.9393618445471649, 0.9556065912926689, 0.967220118643412, 0.8882194173154115, 0.9361538853249073, 0.7872261833965604, 0.8608377368219552, 0.8787718518619395, 0.8169189082396561, 0.7965901553530427, 0.8838665737610132, 0.8844338861256802, 0.9008484784943429, 0.8612318707072047, 0.8623792153658019, 1.0033494995180463, 0.9901213381586231, 0.8780115045650467, 0.9172682690843976, 0.9653905755824115, 0.9199829176728873, 0.9180048223906779, 0.9172043382441968, 0.9267783259554074, 0.9231225672912022, 0.7945054721199195, 0.8655558517080688, 0.8306327906597787, 0.8457559701865576, 0.8038459124570336, 0.8519646989317945, 0.7735358658599594, 0.8612134954656397, 0.8879135146161856]
g_s_m = pd.Series(all_gust_spd_mean_list) #利用Series将列表转换成新的、pandas可处理的数据
g_a_d = pd.Series(all_gust_agl_dev_list)
corr_gust = round(g_s_m.corr(g_a_d), 4) #计算标准差，round(a, 4)是保留a的前四位小数
print('corr_gust :', corr_gust)
#最后画一下两列表散点图，直观感受下，结合相关系数揣摩揣摩
plt.scatter(all_gust_spd_mean_list, all_gust_agl_dev_list)
plt.title('corr_gust :' + str(corr_gust), fontproperties='SimHei') #给图写上title
plt.show()
According to the above procedure, the results obtained:
corr_gust : -0.3481
And image: 
In addition, multiple lists can also calculate the correlation coefficient matrix, i.e., a list of a plurality of correlation coefficients between twenty-two
import pandas as pd
import numpy as np
if __name__ == '__main__':
unstrtf_lst = [[2.136, 1.778, 1.746, 2.565, 1.873, 2.413, 1.813, 1.72, 1.932, 1.987, 2.035, 2.178, 2.05, 2.016, 1.645, 1.756, 1.886, 2.106, 2.138, 1.914, 1.984, 1.906, 1.871, 1.939, 1.81, 1.93, 1.898, 1.802, 2.008, 1.724, 1.823, 1.636, 1.774, 2.055, 1.934, 1.629, 2.519, 2.093, 2.004, 1.793, 1.564, 1.962, 2.176, 1.846, 1.816, 2.018, 1.708, 2.465, 1.899, 1.523, 1.41, 2.102, 2.065, 2.402, 2.091, 1.867, 1.77, 1.466, 2.029, 1.659, 1.626, 1.977, 1.837, 2.13, 2.241, 2.184, 2.345, 1.833, 2.113, 1.764, 1.859, 1.868, 1.835, 1.906, 2.237, 1.846, 1.871, 1.769, 1.928, 1.831, 1.875, 2.039, 2.24, 1.835, 1.851]
, [2.171, 1.831, 1.714, 2.507, 1.793, 2.526, 1.829, 1.705, 1.954, 2.017, 2.022, 2.16, 2.059, 1.966, 1.661, 1.752, 1.884, 2.203, 2.182, 1.97, 2.003, 1.875, 1.852, 1.884, 1.774, 1.916, 1.936, 1.809, 1.926, 1.717, 1.841, 1.59, 1.781, 2.016, 1.898, 1.657, 2.458, 2.134, 2.032, 1.785, 1.575, 1.959, 2.11, 1.854, 1.826, 1.992, 1.706, 2.419, 1.854, 1.514, 1.37, 2.084, 2.024, 2.398, 1.955, 1.859, 1.759, 1.441, 2.059, 1.653, 1.583, 1.987, 1.84, 2.106, 2.262, 2.13, 2.371, 1.776, 2.117, 1.733, 1.814, 1.839, 1.822, 1.883, 2.23, 1.803, 1.894, 1.783, 1.911, 1.813, 1.85, 2.004, 2.191, 1.823, 1.809]
, [2.157, 1.873, 1.802, 2.761, 1.733, 2.506, 1.842, 1.765, 1.938, 2.058, 1.932, 2.196, 2.004, 2.126, 1.664, 1.698, 1.899, 2.073, 2.117, 2.083, 1.972, 1.969, 1.865, 1.937, 1.752, 1.939, 1.927, 1.804, 2.07, 1.725, 1.846, 1.5, 1.804, 2.1, 1.932, 1.773, 2.431, 2.088, 2.08, 1.812, 1.592, 1.953, 2.044, 2.019, 1.846, 2.061, 1.771, 2.254, 1.891, 1.536, 1.356, 1.952, 2.222, 2.427, 2.015, 1.873, 1.79, 1.384, 1.981, 1.665, 1.815, 2.006, 1.869, 2.102, 2.249, 2.27, 2.296, 1.814, 2.099, 1.702, 1.688, 1.89, 1.82, 1.927, 2.162, 1.825, 1.998, 1.811, 2.0, 1.842, 1.793, 2.115, 2.301, 1.789, 1.826]
, [2.127, 1.744, 1.747, 2.548, 1.939, 2.296, 1.808, 1.71, 1.901, 1.906, 2.074, 2.167, 2.113, 2.044, 1.632, 1.821, 1.94, 2.076, 2.114, 1.837, 1.978, 1.904, 1.872, 1.98, 1.886, 1.923, 1.875, 1.799, 1.992, 1.704, 1.812, 1.715, 1.756, 2.061, 1.94, 1.554, 2.592, 2.065, 1.983, 1.802, 1.57, 1.955, 2.215, 1.765, 1.796, 2.006, 1.662, 2.573, 1.915, 1.543, 1.439, 2.16, 2.012, 2.42, 2.268, 1.886, 1.767, 1.527, 2.073, 1.65, 1.567, 2.016, 1.819, 2.153, 2.225, 2.237, 2.327, 1.877, 2.115, 1.804, 1.939, 1.867, 1.84, 1.905, 2.302, 1.883, 1.798, 1.725, 1.893, 1.846, 1.916, 2.025, 2.268, 1.867, 1.877]
, [2.089, 1.664, 1.72, 2.441, 2.031, 2.321, 1.773, 1.702, 1.935, 1.968, 2.119, 2.191, 2.023, 1.925, 1.621, 1.75, 1.822, 2.074, 2.139, 1.764, 1.982, 1.873, 1.895, 1.955, 1.829, 1.945, 1.853, 1.794, 2.046, 1.75, 1.793, 1.741, 1.752, 2.042, 1.965, 1.532, 2.598, 2.086, 1.923, 1.771, 1.517, 1.98, 2.338, 1.743, 1.794, 2.014, 1.693, 2.618, 1.938, 1.5, 1.476, 2.216, 2.003, 2.361, 2.13, 1.85, 1.764, 1.513, 2.001, 1.669, 1.538, 1.897, 1.819, 2.163, 2.226, 2.099, 2.386, 1.865, 2.121, 1.818, 2.0, 1.876, 1.858, 1.908, 2.254, 1.874, 1.791, 1.759, 1.908, 1.822, 1.944, 2.012, 2.201, 1.863, 1.892]
]
column_lst = ['whole_year', 'spring', 'summer', 'autumn', 'winter']
# 计算列表两两间的相关系数
data_dict = {} # 创建数据字典，为生成Dataframe做准备
for col, gf_lst in zip(column_lst, unstrtf_lst):
data_dict[col] = gf_lst
unstrtf_df = pd.DataFrame(data_dict)
cor1 = unstrtf_df.corr() # 计算相关系数，得到一个矩阵
print(cor1)
print(unstrtf_df.columns.tolist())
The results are as follows:
whole_year spring summer autumn winter
whole_year 1.000000 0.986011 0.943254 0.980358 0.965415
spring 0.986011 1.000000 0.944394 0.945710 0.930887
summer 0.943254 0.944394 1.000000 0.876008 0.833568
autumn 0.980358 0.945710 0.876008 1.000000 0.977426
winter 0.965415 0.930887 0.833568 0.977426 1.000000
['whole_year', 'spring', 'summer', 'autumn', 'winter']
[Finished in 0.5s]
Calculating a correlation coefficient numpy
A specific example there is no longer a child, the direct function:
# 这里u1是一个矩阵，可以自己构造，也可以来自dataframe类型：比如u1=a_df.values
np.corrcoef(u1) # 计算矩阵所有行的相关系数
np.corrcoef(u1.T) # 计算矩阵所有列的相关系数
np.around(np.corrcoef(u1), decimals=3) # 这里是将矩阵结果保留3位小数
I have written function calculates the correlation coefficient
Correlation coefficient calculation formula:
import pandas as pd
import math
# 函数：计算相关系数
def calc_corr(a, b):
a_avg = sum(a)/len(a)
b_avg = sum(b)/len(b)
# 计算分子，协方差————按照协方差公式，本来要除以n的，由于在相关系数中上下同时约去了n，于是可以不除以n
cov_ab = sum([(x - a_avg)*(y - b_avg) for x,y in zip(a, b)])
# 计算分母，方差乘积————方差本来也要除以n，在相关系数中上下同时约去了n，于是可以不除以n
sq = math.sqrt(sum([(x - a_avg)**2 for x in a])*sum([(x - b_avg)**2 for x in b]))
corr_factor = cov_ab/sq
return corr_factor
if __name__ == '__main__':
a=[2.1653572007579593, 1.6883696588873887, 1.651425407801895, 2.2299854374330415, 1.7922306220578152, 2.113529406925977, 1.8072576529258022, 1.6619459785959674, 1.8433349117064848, 1.830156003014785, 1.995333114793997, 2.1119786058625314, 2.0885749238172453, 2.0352203568050093, 1.6179657744312377, 1.8171419211111066, 1.7958509222039798, 2.0635601390477394, 2.1215391543655637, 1.8370107139324998, 1.9080529013404595, 1.8142460361148207, 1.8540680605856414, 1.8875508126623393, 1.831566482733203, 1.8780989145241431, 1.8510142426569105, 1.7663870994315451, 1.8119839179642034, 1.6843605863881175, 1.7955302280877627, 1.636906960652483, 1.7194807648617405, 1.9658394488708448, 1.898416417616442, 1.4759321604160809, 2.323803661481871, 1.8769469045284484, 1.8722184196962555, 1.7572526764029732, 1.5557166087197607, 1.8797685204289134, 2.121226143459225, 1.6642017944077512, 1.7472961612960178, 1.9730916979451159, 1.599670318309796, 2.571816259771537, 1.865138228024494, 1.5000996232338855, 1.3928618724470463, 2.2495996610383964, 1.8123728869502833, 2.38651467726676, 2.1662270090414606, 1.849308415655705, 1.723236705400253, 1.526968232018129, 2.073685760455388, 1.6217330036091566, 1.5393098152901363, 1.934097094067261, 1.7724313817029405, 2.0884179221557866, 2.07292956021531, 2.0699873046153954, 2.2232673322791827, 1.7725066092979982, 2.0685477779177055, 1.7725974367148223, 1.9166295058392768, 1.7617609193517068, 1.813075132807376, 1.8257878154680307, 2.2437538529398444, 1.7486220604934224, 1.7108204176980777, 1.7008700704044992, 1.7656760907642393, 1.7865088763312098, 1.8197007789917394, 2.029308787047278, 2.1153740255955116, 1.8164832177052999, 1.7631578858166328]
b=[2.060472329023772, 1.8229873524889462, 1.8340939384974153, 2.7992607339895117, 1.6386430473722386, 2.6863735005361127, 1.837418820905206, 1.6839633919848138, 1.9324516095906468, 2.202357206559493, 1.8845930752558508, 2.236205151912301, 1.8721339205267113, 2.210677601843676, 1.600486811395212, 1.5959949497266086, 1.6935368457211848, 1.9225683452842803, 2.070028267879934, 2.21026368629012, 1.9037935496384224, 1.9327073799581955, 1.829655016157694, 1.871952490421074, 1.7077577799677457, 1.930869014924102, 1.9194158360266231, 1.756182345097486, 2.1850192756376896, 1.7382175288447934, 1.8396075766762512, 1.4219634956892804, 1.8415616922656013, 2.0954072448607093, 1.9126234702257543, 1.9193927496754895, 2.3827058942496806, 1.9648325128486095, 2.0220582287578885, 1.7979299492052836, 1.5496290364943117, 1.910875647672739, 1.9842615165051285, 2.1100210075512824, 1.803544011837867, 2.232027673815636, 1.8934372137054605, 2.1627154441076937, 1.8748707756291958, 1.4668995002228604, 1.3246334541267288, 1.8396494252805005, 2.3688123714848675, 2.336307853359804, 1.9091101911129924, 1.8584589874801458, 1.7573810859876628, 1.2926901611210995, 1.9610407369359222, 1.6523460064563988, 1.754942441907064, 1.9320536352480018, 1.8012104839042546, 2.036906849409057, 2.1802647786624125, 2.191382376122134, 2.2989606091839114, 1.7623619697993311, 2.0639438073684104, 1.6271644912042054, 1.582459595381037, 1.8995793224027187, 1.7507677090017424, 1.9975456593566516, 2.008282198102043, 1.8218912714780746, 2.11945852516168, 1.8031669408615743, 2.175089825880799, 1.8075893333263815, 1.7588846094594992, 2.0752823056821317, 2.1812895620089714, 1.7186172119942524, 1.8537786391164277]
b_s = pd.Series(b)
a_s = pd.Series(a)
cor1 = a_s.corr(b_s)
# 自编函数计算两个列表的相关系数
cor2 = calc_corr(a,b)
# 可以发现两者结果是一样的
print(cor1, cor2)
I write to you, for everyone to recommend a very wide python learning resource gathering, click to enter , there is a senior programmer before learning to share experiences, study notes, there is a chance of business experience, and for everyone to carefully organize a python zero basis to project combat information to you on a daily basis python latest technology, prospects, learning to leave a message of small details
that's all for this article, I hope to learn everyone's help
