已信任 Jupyter 服务器: 本地 Python 3: Not Started [60] import pandas as pd import numpy as np [61] s = pd.Series([877,865,874,890,912]) s 0 877 1 865 2 874 3 890 4 912 dtype: int64 [62] # 想知道每天的变化量,对比的是当天跟昨天的变化量 s.pct_change() 0 NaN 1 -0.013683 2 0.010405 3 0.018307 4 0.024719 dtype: float64 [63] # 协方差 s1 = pd.Series(np.random.randn(10)) s2 = pd.Series(np.random.randn(10)) s1.cov(s2) -0.3417718431113297 [64] # 相关性计算:一个变，另一个是否跟着变 s1 0 0.070405 1 0.155567 2 -0.518001 3 -0.057693 4 0.411682 5 1.841240 6 0.759474 7 0.301355 8 -0.864013 9 0.642086 dtype: float64 [65] s2 = s1*2 [66] s1.corr(s2) 1.0 [67] s3 = pd.Series(np.random.randn(10)) [68] df = pd.DataFrame({ 's1':s1, 's2':s2, 's3':s3 }) df s1 s2 s3 0 0.070405 0.140811 0.771643 1 0.155567 0.311135 2.976528 2 -0.518001 -1.036002 -0.368043 3 -0.057693 -0.115387 0.273931 4 0.411682 0.823364 0.434022 5 1.841240 3.682480 -1.641432 6 0.759474 1.518949 0.682910 7 0.301355 0.602710 0.514268 8 -0.864013 -1.728025 0.023511 9 0.642086 1.284171 0.960029 [69] df.corr() s1 s2 s3 s1 1.000000 1.000000 -0.278755 s2 1.000000 1.000000 -0.278755 s3 -0.278755 -0.278755 1.000000 [-]