%global _empty_manifest_terminate_build 0 Name: python-dynamo-pandas Version: 1.3.0 Release: 1 Summary: Make working with pandas dataframe and AWS DynamoDB easy. License: MIT URL: https://github.com/DrGFreeman/dynamo-pandas Source0: https://mirrors.aliyun.com/pypi/web/packages/eb/ca/15cf7f8dd4ce86b5d1166cf45d9654d3f664231810672a117666651bf190/dynamo-pandas-1.3.0.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-boto3 %description 0 player_id 4 non-null object 1 last_play 4 non-null datetime64[ns] 2 play_time 4 non-null timedelta64[ns] 3 rating 4 non-null float64 4 bonus_points 3 non-null Int8 dtypes: Int8(1), datetime64[ns](1), float64(1), object(1), timedelta64[ns](1) memory usage: 264.0+ bytes ``` Storing the rows of this dataframe to DynamoDB requires multiple data type conversions. ```python >>> from dynamo_pandas import put_df, get_df, keys ``` The `put_df` function adds or updates the rows of a dataframe into the specified table, taking care of the required type conversions (the table must be already created and the primary key column(s) be present in the dataframe). ```python >>> put_df(players_df, table="players") ``` The `get_df` function retrieves the items matching the speficied key(s) from the table into a dataframe. ```python >>> df = get_df(table="players", keys=[{"player_id": "player_three"}, {"player_id": "player_one"}]) >>> print(df) bonus_points player_id last_play rating play_time 0 4 player_three 2021-01-21 10:22:43 2.5 1 days 14:01:19 1 3 player_one 2021-01-18 22:47:23 4.3 2 days 17:41:55 ``` In the case where only a partition key is used, the `keys` function simplifies the generation of the keys list. ```python >>> df = get_df(table="players", keys=keys(player_id=["player_two", "player_four"])) >>> print(df) bonus_points player_id last_play rating play_time 0 1.0 player_two 2021-01-19 19:07:54 3.8 0 days 22:07:34 1 NaN player_four 2021-01-22 13:51:12 4.8 0 days 03:45:49 ``` The data types returned by the `get_df` function are basic types and no automatic type conversion is attempted. ```python >>> df.info() RangeIndex: 2 entries, 0 to 1 Data columns (total 5 columns): %package -n python3-dynamo-pandas Summary: Make working with pandas dataframe and AWS DynamoDB easy. Provides: python-dynamo-pandas BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dynamo-pandas 0 player_id 4 non-null object 1 last_play 4 non-null datetime64[ns] 2 play_time 4 non-null timedelta64[ns] 3 rating 4 non-null float64 4 bonus_points 3 non-null Int8 dtypes: Int8(1), datetime64[ns](1), float64(1), object(1), timedelta64[ns](1) memory usage: 264.0+ bytes ``` Storing the rows of this dataframe to DynamoDB requires multiple data type conversions. ```python >>> from dynamo_pandas import put_df, get_df, keys ``` The `put_df` function adds or updates the rows of a dataframe into the specified table, taking care of the required type conversions (the table must be already created and the primary key column(s) be present in the dataframe). ```python >>> put_df(players_df, table="players") ``` The `get_df` function retrieves the items matching the speficied key(s) from the table into a dataframe. ```python >>> df = get_df(table="players", keys=[{"player_id": "player_three"}, {"player_id": "player_one"}]) >>> print(df) bonus_points player_id last_play rating play_time 0 4 player_three 2021-01-21 10:22:43 2.5 1 days 14:01:19 1 3 player_one 2021-01-18 22:47:23 4.3 2 days 17:41:55 ``` In the case where only a partition key is used, the `keys` function simplifies the generation of the keys list. ```python >>> df = get_df(table="players", keys=keys(player_id=["player_two", "player_four"])) >>> print(df) bonus_points player_id last_play rating play_time 0 1.0 player_two 2021-01-19 19:07:54 3.8 0 days 22:07:34 1 NaN player_four 2021-01-22 13:51:12 4.8 0 days 03:45:49 ``` The data types returned by the `get_df` function are basic types and no automatic type conversion is attempted. ```python >>> df.info() RangeIndex: 2 entries, 0 to 1 Data columns (total 5 columns): %package help Summary: Development documents and examples for dynamo-pandas Provides: python3-dynamo-pandas-doc %description help 0 player_id 4 non-null object 1 last_play 4 non-null datetime64[ns] 2 play_time 4 non-null timedelta64[ns] 3 rating 4 non-null float64 4 bonus_points 3 non-null Int8 dtypes: Int8(1), datetime64[ns](1), float64(1), object(1), timedelta64[ns](1) memory usage: 264.0+ bytes ``` Storing the rows of this dataframe to DynamoDB requires multiple data type conversions. ```python >>> from dynamo_pandas import put_df, get_df, keys ``` The `put_df` function adds or updates the rows of a dataframe into the specified table, taking care of the required type conversions (the table must be already created and the primary key column(s) be present in the dataframe). ```python >>> put_df(players_df, table="players") ``` The `get_df` function retrieves the items matching the speficied key(s) from the table into a dataframe. ```python >>> df = get_df(table="players", keys=[{"player_id": "player_three"}, {"player_id": "player_one"}]) >>> print(df) bonus_points player_id last_play rating play_time 0 4 player_three 2021-01-21 10:22:43 2.5 1 days 14:01:19 1 3 player_one 2021-01-18 22:47:23 4.3 2 days 17:41:55 ``` In the case where only a partition key is used, the `keys` function simplifies the generation of the keys list. ```python >>> df = get_df(table="players", keys=keys(player_id=["player_two", "player_four"])) >>> print(df) bonus_points player_id last_play rating play_time 0 1.0 player_two 2021-01-19 19:07:54 3.8 0 days 22:07:34 1 NaN player_four 2021-01-22 13:51:12 4.8 0 days 03:45:49 ``` The data types returned by the `get_df` function are basic types and no automatic type conversion is attempted. ```python >>> df.info() RangeIndex: 2 entries, 0 to 1 Data columns (total 5 columns): %prep %autosetup -n dynamo-pandas-1.3.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-dynamo-pandas -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 1.3.0-1 - Package Spec generated