details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 8 Computational tools 101 8.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . analysis toolkit, Release 0.7.3 100 Chapter 7. Indexing and selecting data CHAPTER EIGHT COMPUTATIONAL TOOLS 8.1 Statistical functions 8.1.1 Covariance The Series object has a method cov to compute s[’d’] = s[’b’] # so there’s a tie In [175]: s.rank() Out[175]: a 2.0 b 3.5 102 Chapter 8. Computational tools pandas: powerful Python data analysis toolkit, Release 0.7.3 c 1.0 d 3.5 e 5.0 rank

details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 8 Computational tools 95 8.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . data analysis toolkit, Release 0.7.1 94 Chapter 7. Indexing and selecting data CHAPTER EIGHT COMPUTATIONAL TOOLS 8.1 Statistical functions 8.1.1 Covariance The Series object has a method cov to compute s[’d’] = s[’b’] # so there’s a tie In [175]: s.rank() Out[175]: a 2.0 b 3.5 96 Chapter 8. Computational tools pandas: powerful Python data analysis toolkit, Release 0.7.1 c 1.0 d 3.5 e 5.0 rank

details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 8 Computational tools 95 8.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . data analysis toolkit, Release 0.7.2 94 Chapter 7. Indexing and selecting data CHAPTER EIGHT COMPUTATIONAL TOOLS 8.1 Statistical functions 8.1.1 Covariance The Series object has a method cov to compute s[’d’] = s[’b’] # so there’s a tie In [175]: s.rank() Out[175]: a 2.0 b 3.5 96 Chapter 8. Computational tools pandas: powerful Python data analysis toolkit, Release 0.7.2 c 1.0 d 3.5 e 5.0 rank

regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: 439605 NaN 3.092914 f 1.662833 NaN 2.664108 h NaN NaN -0.609235 The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to 4.10. Visualization 607 pandas: powerful Python data analysis toolkit, Release 0.25.3 4.11 Computational tools 4.11.1 Statistical functions Percent change Series and DataFrame have a method pct_change()

regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: 475222 NaN 0.545049 f -1.792440 NaN 0.700366 h NaN NaN 2.189741 The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to ..: Out[194]: {{ header }} 3.12 Computational tools 3.12.1 Statistical functions Percent change Series and DataFrame have a method pct_change()

backends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 2.15 Computational tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: NaN -2.088472 f -4.209138 NaN -0.989859 h NaN NaN -1.413542 The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to

backends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 2.15 Computational tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: NaN -2.088472 f -4.209138 NaN -0.989859 h NaN NaN -1.413542 The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to

directly with matplotlib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 2.12 Computational tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 regard to handling missing data. While NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: NaN -2.088472 f -4.209138 NaN -0.989859 h NaN NaN -1.413542 The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all written to

potentially changes underlying Series dtype . . . . . . . . . . . . . . . . . . . . . 666 14 Computational tools 669 14.1 Statistical Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . analysis toolkit, Release 0.20.3 668 Chapter 13. MultiIndex / Advanced Indexing CHAPTER FOURTEEN COMPUTATIONAL TOOLS 14.1 Statistical Functions 14.1.1 Percent Change Series, DataFrame, and Panel all have 1.123670 NaN 0.018169 b NaN 1.154141 0.305260 c 0.018169 0.305260 1.301149 670 Chapter 14. Computational tools pandas: powerful Python data analysis toolkit, Release 0.20.3 14.1.3 Correlation Several

potentially changes underlying Series dtype . . . . . . . . . . . . . . . . . . . . . 664 xiii 14 Computational tools 667 14.1 Statistical Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . analysis toolkit, Release 0.20.2 666 Chapter 13. MultiIndex / Advanced Indexing CHAPTER FOURTEEN COMPUTATIONAL TOOLS 14.1 Statistical Functions 14.1.1 Percent Change Series, DataFrame, and Panel all have 1.123670 NaN 0.018169 b NaN 1.154141 0.305260 c 0.018169 0.305260 1.301149 668 Chapter 14. Computational tools pandas: powerful Python data analysis toolkit, Release 0.20.2 14.1.3 Correlation Several