ium_470618/dane.ipynb

!kaggle competitions download -c titanic

Downloading titanic.zip to /home/gedin/Studia/InzUczeniaMaszynowego/zadania
100%|███████████████████████████████████████| 34.1k/34.1k [00:00<00:00, 212kB/s]
100%|███████████████████████████████████████| 34.1k/34.1k [00:00<00:00, 212kB/s]

!unzip titanic.zip

Archive:  titanic.zip
  inflating: gender_submission.csv   
  inflating: test.csv                
  inflating: train.csv               

Dane o pliku

!wc -l train.csv
!wc -l test.csv

892 train.csv
419 test.csv

import pandas as pd

df = pd.read_csv("train.csv")

df.head(5)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

df.describe()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

df.hist(["Survived", "Pclass"])

array([[<Axes: title={'center': 'Survived'}>,
        <Axes: title={'center': 'Pclass'}>]], dtype=object)

embarked = df.value_counts("Embarked")
#later will be transformed to one-hot
embarked.plot.bar()

<Axes: xlabel='Embarked'>

# df.dropna()
#df.fillna()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

columns_to_normalize=['Age','Fare']
for colname in columns_to_normalize:
    df[colname]=(df[colname]-df[colname].min())/(df[colname].max()-df[colname].min())

df.head(5)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	0.271174	1	0	A/5 21171	0.014151	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	0.472229	1	0	PC 17599	0.139136	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	0.321438	0	0	STON/O2. 3101282	0.015469	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	0.434531	1	0	113803	0.103644	C123	S
4	5	0	3	Allen, Mr. William Henry	male	0.434531	0	0	373450	0.015713	NaN	S