EDA探索性数据分析

Data exploration

Check the data

import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
train_df = pd.read_csv('train.csv') 
train_df.head()

	id	target	comment_text	severe_toxicity	obscene	identity_attack	insult	threat	asian	atheist	...	article_id	rating	funny	wow	sad	likes	disagree	sexual_explicit	identity_annotator_count	toxicity_annotator_count
0	59848	0.000000	This is so cool. It's like, 'would you want yo...	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	...	2006	rejected	0	0	0	0	0	0.0	0	4
1	59849	0.000000	Thank you!! This would make my life a lot less...	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	...	2006	rejected	0	0	0	0	0	0.0	0	4
2	59852	0.000000	This is such an urgent design problem; kudos t...	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	...	2006	rejected	0	0	0	0	0	0.0	0	4
3	59855	0.000000	Is this something I'll be able to install on m...	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	...	2006	rejected	0	0	0	0	0	0.0	0	4
4	59856	0.893617	haha you guys are a bunch of losers.	0.021277	0.0	0.021277	0.87234	0.0	0.0	0.0	...	2006	rejected	0	0	0	1	0	0.0	4	47

test_df = pd.read_csv('test.csv') 
test_df.head()

id	comment_text
0	7000000	Jeff Sessions is another one of Trump's Orwell...
1	7000001	I actually inspected the infrastructure on Gra...
2	7000002	No it won't . That's just wishful thinking on ...
3	7000003	Instead of wringing our hands and nibbling the...
4	7000004	how many of you commenters have garbage piled ...

对于测试集应该做的就是提取出类似训练集一样的关键字，并对每一样本进行标注，最后进行训练。 

column_name = train_df.columns.values.tolist() 

['id', 'target', 'comment_text', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat', 'asian', 'atheist', 'bisexual', 'black', 'buddhist', 'christian', 'female', 'heterosexual', 'hindu', 'homosexual_gay_or_lesbian', 'intellectual_or_learning_disability', 'jewish', 'latino', 'male', 'muslim', 'other_disability', 'other_gender', 'other_race_or_ethnicity', 'other_religion', 'other_sexual_orientation', 'physical_disability', 'psychiatric_or_mental_illness', 'transgender', 'white', 'created_date', 'publication_id', 'parent_id', 'article_id', 'rating', 'funny', 'wow', 'sad', 'likes', 'disagree', 'sexual_explicit', 'identity_annotator_count', 'toxicity_annotator_count']

train_df_1 = train_df.iloc[:,3:23]
train_df_1.head()

severe_toxicity	obscene	identity_attack	insult	threat	asian	atheist	bisexual	black	buddhist	christian	female	heterosexual	hindu	homosexual_gay_or_lesbian	intellectual_or_learning_disability	jewish	latino	male	muslim
0	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	0.000000	0.0	0.000000	0.00000	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	0.021277	0.0	0.021277	0.87234	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.25	0.0	0.0	0.0	0.0

train_df_2 = train_df.iloc[:,24:44]
train_df_2.head()

	other_gender	other_race_or_ethnicity	other_religion	other_sexual_orientation	physical_disability	psychiatric_or_mental_illness	transgender	white	created_date	publication_id	parent_id	article_id	rating	funny	wow	sad	likes	disagree	sexual_explicit	identity_annotator_count
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2015-09-29 10:50:41.987077+00	2	NaN	2006	rejected	0	0	0	0	0	0.0	0
1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2015-09-29 10:50:42.870083+00	2	NaN	2006	rejected	0	0	0	0	0	0.0	0
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2015-09-29 10:50:45.222647+00	2	NaN	2006	rejected	0	0	0	0	0	0.0	0
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2015-09-29 10:50:47.601894+00	2	NaN	2006	rejected	0	0	0	0	0	0.0	0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2015-09-29 10:50:48.488476+00	2	NaN	2006	rejected	0	0	0	1	0	0.0	4

def missing_data(data):
    total = data.isnull().sum()
    percent = (data.isnull().sum()/data.isnull().count()*100)
    tt = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
    types = []
    for col in data.columns:
        dtype = str(data[col].dtype)
        types.append(dtype)
    tt['Types'] = types
    return(np.transpose(tt))
%%time
missing_data(train_df_1)

	severe_toxicity	obscene	identity_attack	insult	threat	asian	atheist	bisexual	black	buddhist	christian	female	heterosexual	hindu	homosexual_gay_or_lesbian	intellectual_or_learning_disability	jewish	latino	male	muslim
Total	0	0	0	0	0	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744
Percent	0	0	0	0	0	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536
Types	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64

%%time
missing_data(train_df_2)

 

	other_gender	other_race_or_ethnicity	other_religion	other_sexual_orientation	physical_disability	psychiatric_or_mental_illness	transgender	white	created_date	publication_id	parent_id	article_id	rating	funny	wow	sad	likes	disagree	sexual_explicit	identity_annotator_count
Total	1399744	1399744	1399744	1399744	1399744	1399744	1399744	1399744	0	0	778646	0	0	0	0	0	0	0	0	0
Percent	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	77.5536	0	0	43.1413	0	0	0	0	0	0	0	0	0
Types	float64	float64	float64	float64	float64	float64	float64	float64	object	int64	float64	int64	object	int64	int64	int64	int64	int64	float64	int64