Python編程知識——邊寫代碼邊寫筆記，備忘！，python備忘

Python編程知識——邊寫代碼邊寫筆記，備忘！，python備忘

for中的範圍是 [a, b)

for i in range(1,10):    print(i)輸出：123456789

定義空的數組（numpy中的array； list）

X = np.empty(0,dtype=int)Xoutput：array([], dtype=int32)list = []

往數組裡添加元素：

list：append 等等；array：stack , vstack 等等；

去掉一行或者一列：

寫CSV檔案

import pandas as pd#任意的多組列表a = [1,2,3]b = [4,5,6]    #字典中的key值即為csv中列名dataframe = pd.DataFrame({'a_name':a,'b_name':b})#將DataFrame儲存為csv,index表示是否顯示行名，default=Truedataframe.to_csv("test.csv",index=False,sep='')

numpy讀寫檔案

import numpy  my_matrix = numpy.loadtxt(open("c:\\1.csv","rb"),delimiter=",",skiprows=0)  numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ')[source]Save an array to a text file.Parameters: fname : filename or file handleIf the filename ends in .gz, the file is automatically saved in compressed gzip format. loadtxt understands gzipped files transparently.X : array_likeData to be saved to a text file.fmt : str or sequence of strs, optionalA single format (%10.5f), a sequence of formats, or a multi-format string, e.g. ‘Iteration %d – %10.5f’, in which case delimiter is ignored. For complex X, the legal options for fmt are:a single specifier, fmt=’%.4e’, resulting in numbers formattedlike ‘ (%s+%sj)’ % (fmt, fmt)a full string specifying every real and imaginary part, e.g.‘ %.4e %+.4j %.4e %+.4j %.4e %+.4j’ for 3 columnsa list of specifiers, one per column - in this case, the realand imaginary part must have separate specifiers, e.g. [‘%.3e + %.3ej’, ‘(%.15e%+.15ej)’] for 2 columnsdelimiter : str, optionalString or character separating columns.newline : str, optionalString or character separating lines.New in version 1.5.0.header : str, optionalString that will be written at the beginning of the file.New in version 1.7.0.footer : str, optionalString that will be written at the end of the file.New in version 1.7.0.comments : str, optionalString that will be prepended to the header and footer strings, to mark them as comments. Default: ‘# ‘, as expected by e.g. numpy.loadtxt.New in version 1.7.0.

# 畫圖看看資料變動import matplotlib.pyplot as plt%matplotlib inlinex = pro_train[:,0]y = pro_train[:,2]plt.plot(x,y)

將列表格儲存體為csv檔案

import pandas as pdlist_test = [ [1,2,3],[4,5,6],[7,8,9] ]name = ['id','uid','time']test = pd.DataFrame(columns=name,data=list_test)test.to_csv('C:/Users/Admin/Desktop/test.csv')[output]    id  uid time0   1   2   31   4   5   62   7   8   9test2 = pd.DataFrame(data=list_test)test2.to_csv('C:/Users/Admin/Desktop/test2.csv')[output]    0   1   20   1   2   31   4   5   62   7   8   9output = pd.DataFrame( data={"id":test["id"], "sentiment":xgbc_y_predict} )output.to_csv("result/BagOfCentroids_classify_by_XGBoost.csv", index=False, quoting=3 )

numpy.argmax()

>>>a = np.array([[0, 1, 2],\       [3, 4, 5],\       [8, 3, 4]      ])>>>np.argmax(a, axis=1)array([2, 2, 0], dtype=int64)>>>np.argmax(a, axis=0)array([2, 1, 1], dtype=int64)

arr輸出：array([[ 0,  1,  2,  3,  4],       [ 5,  6,  7,  8,  9],       [10, 11, 12, 13, 14]])arr.T輸出：array([[ 0,  5, 10],       [ 1,  6, 11],       [ 2,  7, 12],       [ 3,  8, 13],       [ 4,  9, 14]])

Python之numpy教程（三）：轉置、乘積、通用函數

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