Remove pandas dependency in minimal requirements

Author: Belval

MANIFEST.in

@@ -0,0 +1,3 @@
+include *.txt
+recursive-include extras *.txt
+recursive-include textractor *

NOTICE

@@ -1,2 +1,2 @@
 Amazon Textract Textractor
-Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 
+Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved. 

README.md

@@ -14,13 +14,14 @@ If you are looking for the other amazon-textract-* packages, you can find them u
 
 ## Installation
 
-Textractor is available on PyPI and can be installed with `pip install amazon-textract-textractor`. By default this will install the minimal version of textractor. The following extras can be used to add features:
+Textractor is available on PyPI and can be installed with `pip install amazon-textract-textractor`. By default this will install the minimal version of Textractor which is suitable for lambda execution. The following extras can be used to add features:
 
-- `pdf` (`pip install amazon-textract-textractor[pdf]`) includes `pdf2image` and enables PDF rasterization in Textractor. Note that this is **not** necessary to call Textract with a PDF file.
-- `torch` (`pip install amazon-textract-textractor[torch]`) includes `sentence_transformers` for better word search and matching. This will work on CPU but be noticeably slower than non-machine learning based approaches.
-- `dev` (`pip install amazon-textract-textractor[dev]`) includes all the dependencies above and everything else needed to test the code.
+- `pandas` (`pip install "amazon-textract-textractor[pandas]"`) installs pandas which is used to enable DataFrame and CSV exports.
+- `pdf` (`pip install "amazon-textract-textractor[pdf]"`) includes `pdf2image` and enables PDF rasterization in Textractor. Note that this is **not** necessary to call Textract with a PDF file.
+- `torch` (`pip install "amazon-textract-textractor[torch]"`) includes `sentence_transformers` for better word search and matching. This will work on CPU but be noticeably slower than non-machine learning based approaches.
+- `dev` (`pip install "amazon-textract-textractor[dev]"`) includes all the dependencies above and everything else needed to test the code.
 
-You can pick several extras by separating the labels with commas like this `pip install amazon-textract-textractor[pdf,torch]`.
+You can pick several extras by separating the labels with commas like this `pip install "amazon-textract-textractor[pdf,torch]"`.
 
 ## Documentation
 

docs/source/notebooks/imgs/excel.png

@@ -0,0 +1,2 @@
+numpy==1.21.*
+pandas

docs/source/notebooks/table_data_to_various_formats.ipynb

@@ -1,12 +1,10 @@
-numpy==1.21.*
 awscli
 amazon-textract-response-parser==0.1.33
 amazon-textract-caller==0.0.24
 boto3==1.24.*
 botocore==1.27.90
 jsonschema
 Pillow
-pandas
 tabulate==0.8.10
 XlsxWriter==3.0.3
 pyxDamerauLevenshtein==1.7.1

extras/pandas.txt

@@ -16,7 +16,7 @@ def read_requirements(path):
 setup(
     # include data files
     name="amazon-textract-textractor",
-    version="1.0.5",
+    version="1.0.6",
     description="A package to use AWS Textract services.",
     long_description=long_description,
     long_description_content_type="text/markdown",

requirements.txt

@@ -209,6 +209,7 @@ class AnalyzeIDFields(Enum):
     # Only available in passports
     PLACE_OF_BIRTH = "PLACE_OF_BIRTH"
 
+
 class CLIPrint(Enum):
     ALL = 0
     TEXT = 1
@@ -218,6 +219,7 @@ class CLIPrint(Enum):
     EXPENSES = 5
     IDS = 6
 
+
 class CLIOverlay(Enum):
     ALL = 0
     WORDS = 1