matplotlib绘图

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from numpy.random import randn

path=r'J:论文图集论文数据/玉米重采样1nm数据.xlsx'
data=pd.read_excel(path)

data.iloc[0].head()

WaveLength    qxym4301_000_resamp
338                        0.0206
339                          0.02
340                        0.0194
341                        0.0188
Name: 0, dtype: object

data.head()

	WaveLength	338	339	340	341	342	343	344	345	346	...	2504	2505	2506	2507	2508	2509	2510	2511	2512	2513
0	qxym4301_000_resamp	0.0206	0.0200	0.0194	0.0188	0.0182	0.0179	0.0176	0.0173	0.0175	...	0.1515	0.2020	0.2752	0.3958	0.4624	0.3620	0.2885	0.2958	0.2914	0.2556
1	qxym4301_002_resamp	0.0162	0.0158	0.0153	0.0150	0.0151	0.0152	0.0154	0.0156	0.0156	...	0.1900	0.3139	0.3830	0.3787	0.4345	0.5935	0.6917	0.6336	0.5829	0.5112
2	qxym4302_000_resamp	0.0281	0.0270	0.0261	0.0258	0.0252	0.0247	0.0244	0.0243	0.0244	...	0.3306	0.3650	0.3830	0.3787	0.3343	0.2255	0.1739	0.2538	0.3295	0.4529
3	qxym4302_001_resamp	0.0291	0.0278	0.0269	0.0266	0.0257	0.0249	0.0242	0.0239	0.0241	...	0.2645	0.2920	0.2569	0.1191	0.0501	0.1840	0.2688	0.2252	0.1753	0.0718
4	qxym4401_002_resamp	0.0309	0.0301	0.0293	0.0290	0.0276	0.0269	0.0268	0.0266	0.0263	...	0.0502	0.0184	0.0216	0.0732	0.1273	0.1618	0.1926	0.2259	0.2174	0.0571

5 rows × 2177 columns

data.columns

Index(['WaveLength',          338,          339,          340,          341,
                342,          343,          344,          345,          346,
       ...
               2504,         2505,         2506,         2507,         2508,
               2509,         2510,         2511,         2512,         2513],
      dtype='object', length=2177)

indexes=data.columns[1:]
new_data=data.T.iloc[1:]

new_data.head()

	0	1	2	3	4	5	6	7	8	9	...	150	151	152	153	154	155	156	157	158	159
338	0.0206	0.0162	0.0281	0.0291	0.0309	0.0292	0.0688	0.0198	0.0178	0.0384	...	0.0298	0.0367	0.0355	0.0312	0.0324	0.0329	0.0333	0.0256	0.0272	0.0255
339	0.02	0.0158	0.027	0.0278	0.0301	0.0294	0.0673	0.0197	0.0171	0.0375	...	0.0287	0.0356	0.0345	0.0299	0.0312	0.032	0.0322	0.0249	0.0266	0.0247
340	0.0194	0.0153	0.0261	0.0269	0.0293	0.0289	0.066	0.0196	0.0168	0.0367	...	0.0278	0.0349	0.0337	0.0289	0.0302	0.031	0.0311	0.0243	0.0261	0.0241
341	0.0188	0.015	0.0258	0.0266	0.029	0.0277	0.0651	0.0193	0.0171	0.0365	...	0.0275	0.0348	0.0332	0.0284	0.0296	0.03	0.0303	0.0239	0.0259	0.024
342	0.0182	0.0151	0.0252	0.0257	0.0276	0.0272	0.0645	0.0191	0.0172	0.0357	...	0.0276	0.0344	0.0332	0.0282	0.0291	0.0296	0.0299	0.0238	0.0257	0.0243

5 rows × 160 columns

new_data.index
plt.plot(new_data.T.iloc[0],'g--')
plt.grid()
plt.xlim(350,2000)
plt.ylim(0,0.7)

(0, 0.7)

diff_data=pd.Series(np.diff(new_data[0]).T,index=indexes[:-1])

diff_data[:1500].plot(grid='on',ylim=(-0.008,0.008))

<matplotlib.axes._subplots.AxesSubplot at 0x1c24bd7fac8>

import seaborn as sns
sns.set(style="white")

# Generate a random correlated bivariate dataset
rs = np.random.RandomState(5)
mean = [0, 0]
cov = [(1, .5), (.5, 1)]
x1, x2 = rs.multivariate_normal(mean, cov, 500).T
x1 = pd.Series(x1, name="$X_1$")
x2 = pd.Series(x2, name="$X_2$")

# Show the joint distribution using kernel density estimation
g = sns.jointplot(x1, x2, kind="kde")