============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Thu, 22 Aug 2019 Prob 16:13:35 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which

in the documentation. Our Copyright Policy ==================== PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ===================================================== Dep. Variable: cellphone R-squared: 0.297 Model: OLS Adj. R-squared: 0.274 Method: Least Squares F-statistic: 13.08 Date: Thu, 25 Jul 2013 Prob 15:24:42 Log-Likelihood: -139.16 No. Observations: 33 AIC: 282.3 Df Residuals: 31 BIC: 285.3 Df Model: 1 =============================================================================== coef std err

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:59:49 Log-Likelihood: 247 pandas: powerful Python data analysis toolkit, Release 0.17.0 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important Functionality pandas: powerful Python data analysis toolkit, Release 0.17.0 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 09 Oct 2015 Pseudo R-squ.: 0.6878 Time: 20:16:35 Log-Likelihood:

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Thu, 18 Jul 2019 Prob 17:56:24 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== DataFrame is not intended to be a drop-in replacement for ndarray as its indexing semantics and data model are quite different in places from an n-dimensional array. Series implements __array_ufunc__, which

in the documentation. Our Copyright Policy ==================== PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ===================================================== Dep. Variable: cellphone R-squared: 0.297 Model: OLS Adj. R-squared: 0.274 Method: Least Squares F-statistic: 13.08 Date: Thu, 25 Jul 2013 Prob 15:24:42 Log-Likelihood: -139.16 No. Observations: 33 AIC: 282.3 Df Residuals: 31 BIC: 285.3 Df Model: 1 =============================================================================== coef std err

============================================================================== Dep. Variable: hr R-squared: 0.685 Model: OLS Adj. R-squared: 0.665 Method: Least Squares F-statistic: 34.28 Date: Fri, 25 Jan 2019 Prob 16:28:07 Log-Likelihood: -205.92 No. Observations: 68 AIC: 421.8 Df Residuals: 63 BIC: 432.9 Df Model: 4 Covariance Type: nonrobust =============================================================================== pandas itself only supports IO with a limited set of file formats that map cleanly to its tabular data model. For reading and writing other file formats into and from pandas, we recommend these packages from

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Son, 02 Okt 2016 Pseudo R-squ.: 0.6878 Time: 17:15:45 Log-Likelihood: 349 pandas: powerful Python data analysis toolkit, Release 0.19.0 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Son, 02 Okt 2016 Pseudo R-squ.: 0.6878 Time: 16:19:21 Log-Likelihood:

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Don, 03 Nov 2016 Pseudo R-squ.: 0.6878 Time: 17:08:14 Log-Likelihood: 351 pandas: powerful Python data analysis toolkit, Release 0.19.1 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Don, 03 Nov 2016 Pseudo R-squ.: 0.6878 Time: 16:46:53 Log-Likelihood:

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 07 Jul 2017 Pseudo R-squ.: 0.6878 Time: 12:29:29 Log-Likelihood: 397 pandas: powerful Python data analysis toolkit, Release 0.20.3 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Fri, 07 Jul 2017 Pseudo R-squ.: 0.6878 Time: 12:24:55 Log-Likelihood:

================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Sun, 04 Jun 2017 Pseudo R-squ.: 0.6878 Time: 16:28:52 Log-Likelihood: 395 pandas: powerful Python data analysis toolkit, Release 0.20.2 PyData uses a shared copyright model. Each contributor maintains copyright over their contributions to PyData. However, it is important ================================ Dep. Variable: hr No. Observations: 68 Model: Poisson Df Residuals: 63 Method: MLE Df Model: 4 Date: Sun, 04 Jun 2017 Pseudo R-squ.: 0.6878 Time: 16:24:37 Log-Likelihood: