. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.1 Integration with Apache Parquet file format . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.2 infer_objects type conversion . . . . . . . . . . . . . . . 2012 35 Developer 2013 35.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . . . . . . 2013 36 Internals 2017 36.1 Indexing fixes. We recommend that all users upgrade to this version. Highlights include: • Integration with Apache Parquet, including a new top-level read_parquet() function and DataFrame. to_parquet() method, see

flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these "1357257600000":true,"1357344000000":true}} Fallback behavior If the JSON serializer cannot handle the container contents directly it will fall back in the following manner: • if the dtype is unsupported (e.g if this condition is not satisfied. • labels are ordered. Labels are only read from the first container, it is assumed that each subsequent row / column has been encoded in the same order. This should

. . . . . . . . . . . . . . . . . . . . . . . . . 2476 4.6.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2476 4.7 Policies . . . . . . . . . . . . . . type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2480 4.8.3 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2480 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . . 2476 4.6.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2476 4.7 Policies . . . . . . . . . . . . . . type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2480 4.8.3 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2480 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . . 2531 4.8.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2531 4.9 Policies . . . . . . . . . . . . . . missing value handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 4.10.4 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2536 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . 2674 4.11.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2674 4.12 Policies . . . . . . . . . . . . . . missing value handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4.13.4 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2678 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . 2753 4.11.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2753 4.12 Policies . . . . . . . . . . . . . . missing value handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.4 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . 2753 4.11.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2753 4.12 Policies . . . . . . . . . . . . . . missing value handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4.13.4 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2757 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

. . . . . . . . . . . . . . . . . . . . . . . . . 2525 4.8.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 2525 4.9 Policies . . . . . . . . . . . . . . missing value handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 4.10.4 Apache Arrow interoperability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2530 4 flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these

flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Series is a container for scalars. We would like to be able to insert and remove objects from these "1357257600000":true,"1357344000000":true}} Fallback behavior If the JSON serializer cannot handle the container contents directly it will fall back in the following manner: • if the dtype is unsupported (e.g if this condition is not satisfied. • labels are ordered. Labels are only read from the first container, it is assumed that each subsequent row / column has been encoded in the same order. This should