edapy 0.2.2

[](https://pypi.org/project/edapy/)
[](https://travis-ci.org/MartinThoma/edapy)
[](https://coveralls.io/github/MartinThoma/edapy?branch=master)

edapy is a first resource to analyze a new dataset.

## Installation

```
$ pip install git+https://github.com/MartinThoma/edapy.git
```

For the pdf part, you also need `pdftotext`:

```
$ sudo apt-get install poppler-utils
```


## Usage

```
$ edapy --help
Usage: edapy [OPTIONS] COMMAND [ARGS]...

edapy is a tool for exploratory data analysis with Python.

You can use it to get a first idea what a CSV is about or to get an
overview over a directory of PDF files.

Options:
--version Show the version and exit.
--help Show this message and exit.

Commands:
csv Analyze CSV files.
images Analyze image files.
pdf Analyze PDF files.
```

The workflow is as follows:

* `edapy pdf find --path . --output results.csv` creates a `results.csv`
for you. This `results.csv` contains meta data about all PDF files in the
`path` directory.
* `edapy csv predict --csv_path my-new.csv --types types.yaml` will start /
resume a process in which the user is lead through a series of questions. In
those questions, the user has to decide which delimiter, quotechar is used
and which types the columns have.
* `edapy` generates a `types.yaml` file which can be used to load the CSV in
other applications with `df = edapy.load_csv(csv_path, yaml_path)`.


## Example types.yaml

For the [Titanic Dataset](https://www.kaggle.com/c/titanic/data), the resulting
`types.yaml` looks as follows:

```
columns:
- dtype: other
name: Name
- dtype: int
name: Parch
- dtype: float
name: Age
- dtype: other
name: Ticket
- dtype: float
name: Fare
- dtype: int
name: PassengerId
- dtype: other
name: Cabin
- dtype: other
name: Embarked
- dtype: int
name: Pclass
- dtype: int
name: Survived
- dtype: other
name: Sex
- dtype: int
name: SibSp
csv_meta:
delimiter: ','
```

A sample run then would look like this:

```
$ edapy csv predict --types types_titanik.yaml --csv_path train.csv
Number of datapoints: 891
2018-04-16 21:51:56,279 WARNING Column 'Survived' has only 2 different values ([0, 1]). You might want to make it a 'category'
2018-04-16 21:51:56,280 WARNING Column 'Pclass' has only 3 different values ([3, 1, 2]). You might want to make it a 'category'
2018-04-16 21:51:56,281 WARNING Column 'Sex' has only 2 different values (['male', 'female']). You might want to make it a 'category'
2018-04-16 21:51:56,282 WARNING Column 'SibSp' has only 7 different values ([0, 1, 2, 4, 3, 8, 5]). You might want to make it a 'category'
2018-04-16 21:51:56,283 WARNING Column 'Parch' has only 7 different values ([0, 1, 2, 5, 3, 4, 6]). You might want to make it a 'category'
2018-04-16 21:51:56,285 WARNING Column 'Embarked' has only 3 different values (['S', 'C', 'Q']). You might want to make it a 'category'

## Integer Columns
Column name: Non-nan mean std min 25% 50% 75% max
PassengerId: 891 446.00 257.35 1 224 446 668 891
Survived : 891 0.38 0.49 0 0 0 1 1
Pclass : 891 2.31 0.84 1 2 3 3 3
SibSp : 891 0.52 1.10 0 0 0 1 8
Parch : 891 0.38 0.81 0 0 0 0 6

## Float Columns
Column name: Non-nan mean std min 25% 50% 75% max
Age : 714 29.70 14.53 0.42 20.12 28.00 38.00 80.00
Fare : 891 32.20 49.69 0.00 7.91 14.45 31.00 512.33

## Other Columns
Column name: Non-nan unique top (count)
Name : 891 891 Goldschmidt, Mr. George B (1)
Sex : 891 2 male (577)
Ticket : 891 681 347082 (7)
Cabin : 204 148 C23 C25 C27 (4)
Embarked : 889 4 S (644)
```