Exploratory Data Analysis Open Source ツール


# 探索的データ分析

Explanatory Data Analysisの重要性は

https://qiita.com/Dataiku/items/8e1a1db0b963339dda77



# EDA ツール

CSVデータの例でEDAに関するデータの可視化のツールをまとめます。

## リクワイアメント

+ Download https://www.kaggle.com/c/titanic/data?select=train.csv
+ Install `pip install pandas`

## Dataprep

https://github.com/sfu-db/dataprep

**Install**

```bash
pip install dataprep
```

**Usage**

```python
import pandas as pd
from dataprep.eda import create_report
train = pd.read_csv('train.csv')
create_report(train).show_browser()
```

<details><summary>レポートのSceenshot</summary>

![DataPrep Report.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/fbcdd7f4-c6f8-de92-6542-d60dc8570d34.png)



</details>

---

## Pandas Profiling

https://github.com/ydataai/pandas-profiling

**Install**

```bash
pip install pandas-profiling
```

**Usage**

```python
import pandas as pd
from pandas_profiling import ProfileReport
train = pd.read_csv('train.csv')
profile = ProfileReport(train, title="Report")
profile
# Save as a HTML file
# profile.to_file("pandas_profiling_train.html")
```

<details><summary>レポートのSceenshot</summary>

![Pandas_profiling_Report.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/645627e2-ee52-8bf5-217a-206e479eeadc.png)


</details>

---

## Sweetviz

https://github.com/fbdesignpro/sweetviz

**Install**

```bash
pip install sweetviz
```

**Usage**

```python
import sweetviz as sv
train = pd.read_csv('train.csv')
analyze_report = sv.analyze(train)
analyze_report.show_html('report.html', open_browser=True)
```

<details><summary>レポートのSceenshot</summary>

![Sweetviz_report.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/a95b7fd4-0c65-0c79-7214-0db07a814ed1.png)

</details>

---

## AutoViz

https://github.com/AutoViML/AutoViz

**Install**

```bash
pip install autoviz
```

**Usage**

```python
from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
df_av = AV.AutoViz('train.csv', chart_format='bokeh')

# Local web server
#df_av = AV.AutoViz('train.csv', chart_format='server')

# Save charts as HTML files at AutoViz_Plots/
#df_av = AV.AutoViz('train.csv', chart_format='html')
```

<details><summary>レポートのSceenshot</summary>

![Screenshot 2022-09-27 at 10-28-25 Panel.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/1c4a732a-9328-9b89-fd57-8bb41685bdc0.png)

![Screenshot 2022-09-27 at 10-28-57 Panel.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/e26d6753-24cf-4a4f-e023-10139c606825.png)

![Screenshot 2022-09-27 at 10-29-48 Panel.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/ac98c9ec-a0f0-3f6a-b6e1-f3ebec16bfc2.png)

![Screenshot 2022-09-27 at 10-30-04 Panel.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/aac3e451-632c-3d56-e72c-7fecf42bf7bb.png)

![Screenshot 2022-09-27 at 10-30-13 Panel.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/75eba3b3-f3c2-1494-4faf-d73960c108af.png)

</details>

---

## PipeRider

CLIでEDAとAssertionするツールです

https://github.com/InfuseAI/piperider

**Install**

```bash
pip install 'piperider[csv]'
```

**CLI Usage**

```bash
piperider init

Initialize piperider to path /Users/gabriel/Workspace/playground/titanic/.piperider
[?] What is your data source name? (alphanumeric and underscore are allowed): titanic
[?] Which data source would you like to connect to?: csv
 > csv

Please enter the following fields for csv
[?] Path of csv file: train.csv
```

```bash
piperider run

[?] Do you want to auto generate recommended assertions for this datasource [Yes/no]? Yes

Generating reports from: ~/titanic/.piperider/outputs/latest/run.json
Report generated in ~/titanic/.piperider/outputs/latest/index.html

```

<details><summary>レポートのSceenshot</summary>

![Screenshot 2022-10-12 at 11-18-56 Single-Run Reports PipeRider.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/cdd64559-3878-e96a-f100-5a755a8d4296.png)


![Screenshot 2022-10-12 at 11-19-21 Single-Run Reports PipeRider.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/47eb5b41-f8c4-af87-65f8-576c1f24bb24.png)

![Screenshot 2022-10-12 at 11-21-43 Single-Run Reports PipeRider.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/b66296b2-2259-4014-e92c-a18d2b02dc19.png)


</details>

---

## Whylogs （追記）

https://github.com/whylabs/whylogs

**Install**

```bash
# With Profile Visualizer
pip install 'whylogs[viz]'
```

**Usage**

```python
import whylogs as why
import pandas as pd

#dataframe
train = pd.read_csv("train.csv")
result = why.log(pandas=train)
train_view = result.view()

from whylogs.viz import NotebookProfileVisualizer
visualization = NotebookProfileVisualizer()
visualization.set_profiles(train_view)
visualization.profile_summary()
```

<details><summary>レポートのSceenshot</summary>

![Screen Shot 2022-10-12 at 11.03.06.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/d5132473-3075-8aac-410d-7973839ef8da.png)

</details>



---

## Data Profiler（追記）

https://github.com/capitalone/DataProfiler

AIでEDAすることができるここのProfilerですが、HTMLのレポートがありません。

**Install**

```bash
# Report only
pip install DataProfiler[report]

# With Tensorflow
pip install DataProfiler[ml]

# Full package
pip install DataProfiler[full]

```


**Usage**

```python
import json
from dataprofiler import Data, Profiler

data = Data("train.csv") # Auto-Detect & Load: CSV, AVRO, Parquet, JSON, Text, URL

print(data.data.head(5)) # Access data directly via a compatible Pandas DataFrame

profile = Profiler(data) # Calculate Statistics, Entity Recognition, etc

readable_report = profile.report(report_options={"output_format": "compact"})

print(json.dumps(readable_report, indent=4))
```

<details><summary>レポートのJSON</summary>

```json
{
    "global_stats": {
        "samples_used": 891,
        "column_count": 12,
        "row_count": 891,
        "row_has_null_ratio": 0.7946,
        "row_is_null_ratio": 0.0,
        "unique_row_ratio": 1.0,
        "duplicate_row_count": 0,
        "file_type": "csv",
        "encoding": "utf-8",
        "correlation_matrix": null,
        "chi2_matrix": "[[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], ... , [nan,  0.,  0., nan,  0.,  0.,  0.,  0., nan, nan, nan,  1.]]",
        "profile_schema": {
            "PassengerId": [
                0
            ],
            "Survived": [
                1
            ],
            "Pclass": [
                2
            ],
            "Name": [
                3
            ],
            "Sex": [
                4
            ],
            "Age": [
                5
            ],
            "SibSp": [
                6
            ],
            "Parch": [
                7
            ],
            "Ticket": [
                8
            ],
            "Fare": [
                9
            ],
            "Cabin": [
                10
            ],
            "Embarked": [
                11
            ]
        },
        "times": {
            "row_stats": 0.003
        }
    },
    "data_stats": [
        {
            "column_name": "PassengerId",
            "data_type": "int",
            "categorical": false,
            "order": "ascending",
            "samples": "['600', '74', '831', '76', '811']",
            "statistics": {
                "min": 1.0,
                "max": 891.0,
                "mode": "[1.445, 2.335, 3.225, 4.115, 5.005]",
                "median": 446.445,
                "sum": 397386.0,
                "mean": 446.0,
                "variance": 66231.0,
                "stddev": 257.3538,
                "skewness": 0.0,
                "kurtosis": -1.2,
                "quantiles": {
                    "0": 223.2775,
                    "1": 446.445,
                    "2": 668.7225
                },
                "median_abs_deviation": 222.7225,
                "num_zeros": 0,
                "num_negatives": 0,
                "unique_count": 891,
                "unique_ratio": 1.0,
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 1.0,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Survived",
            "data_type": "int",
            "categorical": true,
            "order": "random",
            "samples": "['1', '0', '0', '1', '1']",
            "statistics": {
                "min": 0.0,
                "max": 1.0,
                "mode": "[0.0005]",
                "median": 0.0008,
                "sum": 342.0,
                "mean": 0.3838,
                "variance": 0.2368,
                "stddev": 0.4866,
                "skewness": 0.4785,
                "kurtosis": -1.775,
                "quantiles": {
                    "0": 0.0004,
                    "1": 0.0008,
                    "2": 0.9993
                },
                "median_abs_deviation": 0,
                "num_zeros": 549,
                "num_negatives": 0,
                "unique_count": 2,
                "unique_ratio": 0.0022,
                "categories": "['0', '1']",
                "gini_impurity": 0.473,
                "unalikeability": 0.4735,
                "categorical_count": {
                    "0": 549,
                    "1": 342
                },
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 1.0,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Pclass",
            "data_type": "int",
            "categorical": true,
            "order": "random",
            "samples": "['2', '2', '1', '1', '3']",
            "statistics": {
                "min": 1.0,
                "max": 3.0,
                "mode": "[2.999]",
                "median": 2.9982,
                "sum": 2057.0,
                "mean": 2.3086,
                "variance": 0.699,
                "stddev": 0.8361,
                "skewness": -0.6305,
                "kurtosis": -1.28,
                "quantiles": {
                    "0": 2.0001,
                    "1": 2.9982,
                    "2": 2.9991
                },
                "median_abs_deviation": 0.0016,
                "num_zeros": 0,
                "num_negatives": 0,
                "unique_count": 3,
                "unique_ratio": 0.0034,
                "categories": "['3', '1', '2']",
                "gini_impurity": 0.5949,
                "unalikeability": 0.5956,
                "categorical_count": {
                    "3": 491,
                    "1": 216,
                    "2": 184
                },
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 1.0,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Name",
            "data_type": "string",
            "categorical": false,
            "order": "random",
            "samples": "['Smiljanic, Mr. Mile', 'Isham, Miss. Ann Elizabeth',\n 'Petranec, Miss. Matilda', 'Ling, Mr. Lee',\n 'Kirkland, Rev. Charles Leonard']",
            "statistics": {
                "min": 12.0,
                "max": 82.0,
                "mode": "[19.035]",
                "median": 25.0041,
                "sum": 24026.0,
                "mean": 26.9652,
                "variance": 86.1482,
                "stddev": 9.2816,
                "skewness": 1.3926,
                "kurtosis": 2.5594,
                "quantiles": {
                    "0": 20.0137,
                    "1": 25.0041,
                    "2": 30.0586
                },
                "median_abs_deviation": 5.0216,
                "vocab": "['O', ' ', 'k', 'a', 'P', ... , 'K', 'j', 'd', 'Q', 'x']",
                "unique_count": 891,
                "unique_ratio": 1.0,
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.0,
                    "float": 0.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Sex",
            "data_type": "string",
            "categorical": true,
            "order": "random",
            "samples": "['male', 'male', 'female', 'male', 'male']",
            "statistics": {
                "min": 4.0,
                "max": 6.0,
                "mode": "[4.001]",
                "median": 4.0015,
                "sum": 4192.0,
                "mean": 4.7048,
                "variance": 0.9139,
                "stddev": 0.956,
                "skewness": 0.6189,
                "kurtosis": -1.6206,
                "quantiles": {
                    "0": 4.0008,
                    "1": 4.0015,
                    "2": 5.9986
                },
                "median_abs_deviation": 0,
                "vocab": "['e', 'm', 'a', 'f', 'l']",
                "unique_count": 2,
                "unique_ratio": 0.0022,
                "categories": "['male', 'female']",
                "gini_impurity": 0.4564,
                "unalikeability": 0.4569,
                "categorical_count": {
                    "male": 577,
                    "female": 314
                },
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.0,
                    "float": 0.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Age",
            "data_type": "float",
            "categorical": true,
            "order": "random",
            "samples": "['28', '34', '27', '26', '63']",
            "statistics": {
                "min": 0.42,
                "max": 80.0,
                "mode": "[24.01547]",
                "median": 28.0183,
                "sum": 21205.17,
                "mean": 29.6991,
                "variance": 211.0191,
                "stddev": 14.5265,
                "skewness": 0.3891,
                "kurtosis": 0.1783,
                "quantiles": {
                    "0": 20.0736,
                    "1": 28.0183,
                    "2": 38.0505
                },
                "median_abs_deviation": 8.9421,
                "num_zeros": 0,
                "num_negatives": 0,
                "precision": {
                    "min": 1,
                    "max": 3,
                    "mean": 1.85,
                    "var": 0.18,
                    "std": 0.424,
                    "sample_size": 714,
                    "margin_of_error": 0.0523,
                    "confidence_level": 0.999
                },
                "unique_count": 88,
                "unique_ratio": 0.1232,
                "categories": "['24', '22', '18', '19', ... , '55.5', '0.92', '23.5', '74']",
                "gini_impurity": 0.978,
                "unalikeability": 0.9794,
                "categorical_count": {
                    "24": 30,
                    "22": 27,
                    "18": 26,
                    "19": 25,
                    "28": 25,
                    "30": 25,
                    "21": 24,
                    "25": 23,
                    "36": 22,
                    "29": 20,
                    "32": 18,
                    "35": 18,
                    "27": 18,
                    "26": 18,
                    "16": 17,
                    "31": 17,
                    "20": 15,
                    "34": 15,
                    "33": 15,
                    "23": 15,
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                    "17": 13,
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                    "37": 6,
                    "49": 6,
                    "41": 6,
                    "3": 6,
                    "58": 5,
                    "15": 5,
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                    "56": 4,
                    "5": 4,
                    "11": 4,
                    "60": 4,
                    "8": 4,
                    "6": 3,
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                    "61": 3,
                    "65": 3,
                    "7": 3,
                    "10": 2,
                    "64": 2,
                    "13": 2,
                    "63": 2,
                    "30.5": 2,
                    "57": 2,
                    "70": 2,
                    "0.75": 2,
                    "71": 2,
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                    "0.83": 2,
                    "40.5": 2,
                    "55": 2,
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                    "28.5": 2,
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                    "34.5": 1,
                    "0.42": 1,
                    "0.67": 1,
                    "66": 1,
                    "24.5": 1,
                    "80": 1,
                    "20.5": 1,
                    "53": 1,
                    "14.5": 1,
                    "70.5": 1,
                    "12": 1,
                    "36.5": 1,
                    "55.5": 1,
                    "0.92": 1,
                    "23.5": 1,
                    "74": 1
                },
                "sample_size": 891,
                "null_count": 177,
                "null_types": "['']",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.965,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "SibSp",
            "data_type": "int",
            "categorical": true,
            "order": "random",
            "samples": "['1', '0', '0', '5', '0']",
            "statistics": {
                "min": 0.0,
                "max": 8.0,
                "mode": "[0.004]",
                "median": 0.0059,
                "sum": 466.0,
                "mean": 0.523,
                "variance": 1.216,
                "stddev": 1.1027,
                "skewness": 3.6954,
                "kurtosis": 17.8804,
                "quantiles": {
                    "0": 0.0029,
                    "1": 0.0059,
                    "2": 1.0023
                },
                "median_abs_deviation": 0,
                "num_zeros": 608,
                "num_negatives": 0,
                "unique_count": 7,
                "unique_ratio": 0.0079,
                "categories": "['0', '1', '2', '4', '3', '8', '5']",
                "gini_impurity": 0.4775,
                "unalikeability": 0.4781,
                "categorical_count": {
                    "0": 608,
                    "1": 209,
                    "2": 28,
                    "4": 18,
                    "3": 16,
                    "8": 7,
                    "5": 5
                },
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 1.0,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Parch",
            "data_type": "int",
            "categorical": true,
            "order": "random",
            "samples": "['1', '0', '0', '0', '0']",
            "statistics": {
                "min": 0.0,
                "max": 6.0,
                "mode": "[0.003]",
                "median": 0.0039,
                "sum": 340.0,
                "mean": 0.3816,
                "variance": 0.6497,
                "stddev": 0.8061,
                "skewness": 2.7491,
                "kurtosis": 9.7781,
                "quantiles": {
                    "0": 0.002,
                    "1": 0.0039,
                    "2": 0.0059
                },
                "median_abs_deviation": 0,
                "num_zeros": 678,
                "num_negatives": 0,
                "unique_count": 7,
                "unique_ratio": 0.0079,
                "categories": "['0', '1', '2', '5', '3', '4', '6']",
                "gini_impurity": 0.3953,
                "unalikeability": 0.3957,
                "categorical_count": {
                    "0": 678,
                    "1": 118,
                    "2": 80,
                    "5": 5,
                    "3": 5,
                    "4": 4,
                    "6": 1
                },
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 1.0,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Ticket",
            "data_type": "string",
            "categorical": false,
            "order": "random",
            "samples": "['113781', '315097', '371110', 'C.A. 17248', '36947']",
            "statistics": {
                "min": 3.0,
                "max": 18.0,
                "mode": "[6.0075]",
                "median": 6.0076,
                "sum": 6015.0,
                "mean": 6.7508,
                "variance": 7.5379,
                "stddev": 2.7455,
                "skewness": 2.211,
                "kurtosis": 5.1754,
                "quantiles": {
                    "0": 5.0087,
                    "1": 6.0076,
                    "2": 6.9985
                },
                "median_abs_deviation": 0.9972,
                "vocab": "['H', '5', '7', 'Q', '3', ... , '0', 'P', 'e', 'L', 'N']",
                "unique_count": 681,
                "unique_ratio": 0.7643,
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.4837,
                    "int": 0.7419,
                    "float": 0.7419,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Fare",
            "data_type": "float",
            "categorical": false,
            "order": "random",
            "samples": "['7.8958', '9.5', '0', '34.375', '227.525']",
            "statistics": {
                "min": 0.0,
                "max": 512.3292,
                "mode": "[7.9411026]",
                "median": 14.5475,
                "sum": 28693.9493,
                "mean": 32.2042,
                "variance": 2469.4368,
                "stddev": 49.6934,
                "skewness": 4.7873,
                "kurtosis": 33.3981,
                "quantiles": {
                    "0": 8.0222,
                    "1": 14.5475,
                    "2": 31.124
                },
                "median_abs_deviation": 6.945,
                "num_zeros": 15,
                "num_negatives": 0,
                "precision": {
                    "min": 0,
                    "max": 7,
                    "mean": 3.899,
                    "var": 2.3898,
                    "std": 1.5459,
                    "sample_size": 891,
                    "margin_of_error": 0.1704,
                    "confidence_level": 0.999
                },
                "unique_count": 248,
                "unique_ratio": 0.2783,
                "sample_size": 891,
                "null_count": 0,
                "null_types": "[]",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.1807,
                    "float": 1.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Cabin",
            "data_type": "string",
            "categorical": false,
            "order": "random",
            "samples": "['B42', 'E25', 'D', 'E33', 'A23']",
            "statistics": {
                "min": 1.0,
                "max": 15.0,
                "mode": "[2.995]",
                "median": 2.9961,
                "sum": 732.0,
                "mean": 3.5882,
                "variance": 4.3025,
                "stddev": 2.0743,
                "skewness": 3.1847,
                "kurtosis": 11.7603,
                "quantiles": {
                    "0": 2.9905,
                    "1": 2.9961,
                    "2": 3.0017
                },
                "median_abs_deviation": 0.0056,
                "vocab": "['5', '7', '3', 'T', 'E', ... , '1', 'D', '4', ' ', '0']",
                "unique_count": 147,
                "unique_ratio": 0.7206,
                "sample_size": 891,
                "null_count": 687,
                "null_types": "['']",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.0,
                    "float": 0.0,
                    "string": 1.0
                }
            }
        },
        {
            "column_name": "Embarked",
            "data_type": "string",
            "categorical": true,
            "order": "random",
            "samples": "['S', 'S', 'S', 'S', 'S']",
            "statistics": {
                "min": 1.0,
                "max": 1.0,
                "mode": "[1.]",
                "median": 1.0,
                "sum": 889.0,
                "mean": 1.0,
                "variance": 0.0,
                "stddev": 0.0,
                "skewness": 0.0,
                "kurtosis": -3.0102,
                "quantiles": {
                    "0": 1.0,
                    "1": 1.0,
                    "2": 1.0
                },
                "median_abs_deviation": 0,
                "vocab": "['S', 'Q', 'C']",
                "unique_count": 3,
                "unique_ratio": 0.0034,
                "categories": "['S', 'C', 'Q']",
                "gini_impurity": 0.432,
                "unalikeability": 0.4325,
                "categorical_count": {
                    "S": 644,
                    "C": 168,
                    "Q": 77
                },
                "sample_size": 891,
                "null_count": 2,
                "null_types": "['']",
                "data_type_representation": {
                    "datetime": 0.0,
                    "int": 0.0,
                    "float": 0.0,
                    "string": 1.0
                }
            }
        }
    ]
}

```

</details>

---

## Lux（追記）

https://github.com/lux-org/lux


**Install**

```bash
pip install lux-api
```

**Usage**

```python
import lux
import pandas as pd
df = pd.read_csv("train.csv")
df
# df.save_as_html("lux.html")
```


<details><summary>レポートのSceenshot</summary>

![Screenshot 2022-09-28 at 10-17-10 Lux Widget.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/3546cf76-0301-e1c1-9fae-4caff8203cd7.png)

![Screenshot 2022-09-28 at 10-17-42 Lux Widget.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/ba543dca-10c9-2f46-2b11-47df10d3b155.png)

![Screenshot 2022-09-28 at 10-19-01 Lux Widget.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/58e4f415-3faa-7402-a7be-6090b51aec55.png)

</details>

---

## Polymersearch（追記）

Webでspreadsheetやcsvを読み込んでEDAします

https://www.polymersearch.com/

:::note info
本文記載時点は無料で14-days-trialがあります
:::

<details><summary>レポートのSceenshot</summary>

![Screenshot 2022-09-27 at 11-58-25 train.csv.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/5e53eb1b-432c-f654-f11a-98cf9d7aa108.png)

![Screenshot 2022-09-27 at 12-00-28 train.csv.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1623438/85312307-f6d1-796f-e857-256e3fc1d7bd.png)

</details>