pandas
Rã§ç§»åå¹³å - tetsunosukeã®notebook ã pandasã§ãä»åã¯çµæ§ç°¡åã§ãã >>> import numpy as np >>> import pandas as pd >>> data = np.array([1,2,3,4,5,4,3,2,1]) >>> pd.rolling_mean(data, 5) array([ nan, nan, nan, nan, 3. , 3.6, 3.8, 3.6, 3. ])
Rã§å帰åæ - tetsunosukeã®notebook ã pandasã§ãã£ã¦ã¿ãç¹ã«ãã¡ã¤ã«ãèªã¿è¾¼ãé¨åãRã£ã½ãæ¸ãã import pandas as pd >>> data = pd.read_csv("2-1.csv") >>> data.describe() degree amount count 12.000000 12.000000 mean 16.391667 1310.833333â¦
Rã§ã«ã¤äºä¹æ¤å® ãPythonã§è¡ãã¾ããã«ã¤äºä¹æ¤å®ãè¡ãã ãã§ããã°pandasã¯å¿ è¦ãªãããã§ããRã§ä¸è¨ã®ããã«è¡ã£ã¦ããã³ã¼ããã chisq.test( c(10, 10, 10, 25, 1, 4) ) scipy.stats.chi2_contingencyã«æ¸¡ãããã«ãæ¬æ¥åæ°ã§ããã°å ¨é¨10åãã¤ã«â¦