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%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()
<class 'pandas.core.frame.DataFrame'>
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()
<class 'pandas.core.frame.DataFrame'>
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()
<class 'pandas.core.frame.DataFrame'>
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 <Python_Bot@openeuler.org> - 1.3.0-1
- Package Spec generated
|
