一個小例子集合xlrd，matplotlib，numpy，scipy使用方法（從Excel導入數據）

最近因爲一篇論文的原因，要從Excel中取得部分數據平作圖，但是得到的圖都是位圖，不是太好插入到論文中，因此，決定使用Python畫圖來解決此問題（不使用MATLAB的原因在於它畫出的圖是在是不好看呀）

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首先使用的庫是xlrd庫，此庫的作用是從讀取Excel數據

#對應的所有例子中使用的方法貼到了文章最後，方便查找
import xlrd
#首先實例化一個book對象
book = xlrd.open_workbook("一個Excel地址")
#然後獲取這個book的所有sheet名稱,返回一個列表
sheetName = book.sheet_names()
#獲取sheet類的一個對象，也就是說，如果知道每個sheet的名字的話，就可以直接用sheet的名字來實例化它了
sheet1 = book.sheet_by_name（sheetName[0]）
#然後我們去獲得這個對象的列的數據,比如下面一行獲得是sheet1也就是第一個sheet的第一列的前40個數據，格式爲一個列表
data_col = sheet1.col_values(0)[0:40]

#到目前爲止，我們已經實現了獲得數據的目的

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然後使用的是庫numpy，matplotlib庫

import numpy as np
import matplotlib,pyplot as plt
#我們利用上面得到的數據進行處理
#生成一個X軸array
x = np.arange(1,len(data_col)+1,1)
#利用導入的Excel表格獲得的list類型的數據賦值給y
y = np.array(data_col)
#繪製x,y的圖像
plt.plot(x,y,label = "test",fontsize = 15)
#添加橫座標
plt.xlabel("time")
添加縱座標
plt.ylabel("displacement")
#添加legend
plt.legend(loc = 0）
plt.show()

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期間，會用到scipy庫，目的是smooth曲線

from scipy.interpolate import spline
#使用方法如下
xnew = np.linspace(x.min(),x.max(),300)
ynew = spline(x,y,xnew)
#現在就可以使用如下方式畫出smooth的圖
plt.plot(xnew,ynew)

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如何實現一個畫布多個圖以及雙Y軸圖的繪製

#實現一個畫布多個圖的方法
#plt.subplot（rows,cols,num）,需要繪製的每個圖之前使用這個，然後修改代表每個圖位置的第三個參數
plt.subplot（2,2,1）

#實現雙Y軸的方法
plt.twinx()

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附上一個完整的例子，代碼寫的不規範

import matplotlib.pyplot as plt
import numpy as np
import xlrd
from scipy.interpolate import spline

book = xlrd.open_workbook("國網數據-位移-加速度.xls")
sheet = book.sheet_names()[0:4]
# print(sheet)
sheet_con = []
for data in sheet:
    sheet_con.append(book.sheet_by_name(data))
# print(sheet_con)
i = 0
for all_data in sheet_con:
    y1 = all_data.col_values(0)[1:40]
    y2 = all_data.col_values(1)[1:40]
    x = np.arange(1,len(y1)+1,1)
    i = i + 1
    xnew = np.linspace(x.min(),x.max(),600)
    y1 = spline(x,y1,xnew)
    y2 = spline(x,y2,xnew)
    plt.subplot(2,2,i)
    plt.plot(xnew,y1,"b",linewidth = 2,label = "acceleration")
    plt.legend(loc = 2)
    plt.xlabel("time/(0.1s)")
    if i == 1 or i == 3:
        plt.ylabel("acceleration of X/(g)")
    else:
        plt.ylabel("acceleration of Z/(g)")
    plt.twinx()
    plt.plot(xnew,y2,"r--",linewidth = 2,label = "displacement")
    plt.legend(loc = 3)
    if i == 1 or i == 2:
        plt.ylabel("displacement of label 1/(mm)")
    else:
        plt.ylabel("displacement of label 2/(mm)")
# plt.title("relationship between acceleration and displacement")

plt.show()

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xlrd有關方法簡介

#獲取這個sheet_name對應的sheet對象
sheet_by_name(sheet_name) [#]
    sheet_name
    Name of sheet required
    Returns:
    An object of the Sheet class

#獲取book對象中所有的sheet的名字（返回列表形式）
sheet_names() [#]
    Returns:
    A list of the names of the sheets in the book.

#獲取book中所有sheet的地址
sheets() [#]
    Returns:
    A list of all sheets in the book.

col_slice(colx, start_rowx=0, end_rowx=None) [#]
    Returns a slice of the Cell objects in the given column.

col_types(colx, start_rowx=0, end_rowx=None) [#]
    Returns a slice of the types of the cells in the given column.

#sheet對象的一個方法，獲取此sheet對象的第colx列，從start_rowx到end_rowx中所有data的列表
col_values(colx, start_rowx=0, end_rowx=None) [#]
    Returns a slice of the values of the cells in the given column.

name [#]
    Name of sheet.

#獲取sheet對象中column的列數
ncols [#]
    Number of columns in sheet. A column index is in range(thesheet.ncols).

#獲取sheet對象中row行數
nrows [#]
    Number of rows in sheet. A row index is in range(thesheet.nrows).

row(rowx) [#]
    Returns a sequence of the Cell objects in the given row.

row_label_ranges [#]
    List of address ranges of cells containing row labels. For more details, see col_label_ranges above. 
    -- New in version 0.6.0

row_slice(rowx, start_colx=0, end_colx=None) [#]
    Returns a slice of the Cell objects in the given row.

row_types(rowx, start_colx=0, end_colx=None) [#]
    Returns a slice of the types of the cells in the given row.

row_values(rowx, start_colx=0, end_colx=None) [#]
    Returns a slice of the values of the cells in the given row.
以上摘自http://www.lexicon.net/sjmachin/xlrd.html

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這是一個關於numpy與matplotlib的提及方法簡介

#獲得一個初值，終值，步長的array
numpy.arange([start, ]stop, [step, ])
    Return evenly spaced values within a given interval.

#傳入一個list直接生成array
numpy.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
    return an array.
    object : array_like——An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.

#label指的是次線條的標籤，linewieth是線寬，fontsize是字體大小
plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2，fontsize = 15)

#xlabel設定
matplotlib.pyplot.xlabel(s, *args, **kwargs)
    Set the x axis label of the current axis.
    Default override is:
    override = {
        'fontsize'            : 'small',
        'verticalalignment'   : 'top',
        'horizontalalignment' : 'center'
        }

#legend設定，location的數字代表的位置
plt.legend(loc = 0)

Location String	Location Code
‘best’	0
‘upper right’	1
‘upper left’	2
‘lower left’	3
‘lower right’	4
‘right’	5
‘center left’	6
‘center right’	7
‘lower center’	8
‘upper center’	9
‘center’	10

#生成一個初值終值爲範圍，以num爲均勻的間隔點的一個樣本
numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)
    Return evenly spaced numbers over a specified interval.
    Returns num evenly spaced (間隔均勻的) samples, calculated over the interval [start, stop].
    The endpoint of the interval can optionally be excluded.

#一畫布多圖片
matplotlib.pyplot.subplot(*args, **kwargs)
    Return a subplot axes positioned by the given grid definition.
    Typical call signature:
    #畫布中，圖像的分佈位置
    subplot(nrows, ncols, plot_number)
    Where nrows and ncols are used to notionally split the figure into nrows * ncols sub-axes, and plot_number is used to identify the particular subplot that this function is to create within the notional grid. plot_number starts at 1, increments across rows first and has a maximum of nrows * ncols.
    In the case when nrows, ncols and plot_number are all less than 10, a convenience exists, such that the a 3 digit number can be given instead, where the hundreds represent nrows, the tens represent ncols and the units represent plot_number

#雙Y軸方法的實現
matplotlib.pyplot.twinx(ax=None)
    Make a second axes that shares the x-axis. The new axes will overlay ax (or the current axes if ax is None). The ticks for ax2 will be placed on the right, and the ax2 instance is returned.

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關於scipy的方法

scipy.interpolate.spline(*args, **kwds)[source]
Interpolate a curve at new points using a spline fit
Parameters: 
    xk, yk : array_like
    The x and y values that define the curve.
    xnew : array_like
    The x values where spline should estimate the y values.
地址：https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#spline-interpolation