Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis applied on __enter__ (GH8514) • Bug in resample that causes a ValueError when resampling across multiple days and the last offset is not calcu- lated from the start of the range (GH8683) • Bug where DataFrame

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis than NaN (GH7900) • Bug in ewmstd(), ewmvol(), ewmvar(), and ewmcov() calculation of de-biasing factors when bias=False (the default). Previously an incorrect constant factor was used, based on adjust=True

extensible API.  Minimal structure - easy to achieve the result without any frills.  It supports multiple platforms and backends.  It is user friendly framework which runs on both CPU and GPU.  Network (ANN) was invented by psychologist Frank Rosenblatt, in the year of 1958. ANNs are made up of multiple nodes which is similar to neurons. Nodes are tightly interconnected and organized into different represented as below: 4. Keras ― Overview of Deep learning Keras 12 Here,  Multiple input along with weight represents dendrites.  Sum of input along with activation function represents

they are designed to provide. Four consultants over a period of five weeks along with the help of multiple shadows (provided at no additional cost) worked on the project in tight partnership with Google’s risk, application’s exposure and user population, technical difficulty of exploitation, and other factors. For an explanation of NCC Group’s risk rating and finding categorization, see Appendix A on page risk, application’s exposure and user population, technical difficulty of exploitation, and other factors. The risk rating is NCC Group’s recommended prioritization for addressing findings. Every organization

(Contd.) Feng Li (SDU) Overview September 6, 2023 30 / 57 Unsupervised Learning: Discovering Latent Factors Dimensionality reduction When dealing with high dimensional data, it is often useful to reduce dimensional, there may only be a small number of degrees of variability, corresponding to latent factors Feng Li (SDU) Overview September 6, 2023 31 / 57 Unsupervised Learning: Discovering Graph Structures probabilities can be used to infer uncertainty. A one-vs-one SVM approach can be used to tackle multiple classes. Feng Li (SDU) Overview September 6, 2023 47 / 57 Parametric vs Non-Parametric Models

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis keyword values. (GH10633) • Bug in DataFrame.plot raises ValueError when color name is specified by multiple characters (GH10387) • Bug in Index construction with a mixed list of tuples (GH10697) • Bug in

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis iterator=boolean, and chunksize=number_in_a_chunk are provided to sup- port iteration on select and select_as_multiple (GH3076) • You can now select timestamps from an unordered timeseries similarly to an ordered timeseries

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 0.7.2 cleaning attributes on GroupBy (GH882) • Can pass dict of values to DataFrame.fillna (GH661) • Can select multiple hierarchical groups by passing list of values in .ix (GH134) • Add axis option to DataFrame.fillna

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 0.7.1 cleaning append and DataFrame.append (GH468, GH479, GH273) • Can pass multiple DataFrames to DataFrame.append to concatenate (stack) and multiple Series to Series.append too • Can pass list of dicts (e.g., a

Flexible reshaping and pivoting of data sets • Hierarchical labeling of axes (possible to have multiple labels per tick) • Robust IO tools for loading data from flat files (CSV and delimited), Excel scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 0.7.3 cleaning attributes on GroupBy (GH882) • Can pass dict of values to DataFrame.fillna (GH661) • Can select multiple hierarchical groups by passing list of values in .ix (GH134) • Add axis option to DataFrame.fillna