摘要：引入包和加載數(shù)據(jù)清洗數(shù)據(jù)查看數(shù)據(jù)維度以及類型缺失值處理查看數(shù)據(jù)統(tǒng)計(jì)信息數(shù)值屬性離散化計(jì)算特征與屬性之間關(guān)系查看數(shù)據(jù)維度以及類型查看前五條數(shù)據(jù)查看每列數(shù)據(jù)類型以及情況獲得所有屬性查看數(shù)據(jù)統(tǒng)計(jì)信息查看連續(xù)數(shù)值屬性基本統(tǒng)計(jì)情況查看屬性數(shù)據(jù)統(tǒng)計(jì)情況

引入包和加載數(shù)據(jù)

1
2
3
4
5
import pandas as pd
import numpy as np
train_df =pd.read_csv("../datas/train.csv") # train set
test_df = pd.read_csv("../datas/test.csv") # test set
combine = [train_df, test_df]

清洗數(shù)據(jù)

查看數(shù)據(jù)維度以及類型
缺失值處理
查看object數(shù)據(jù)統(tǒng)計(jì)信息
數(shù)值屬性離散化
計(jì)算特征與target屬性之間關(guān)系

查看數(shù)據(jù)維度以及類型

1
2
3
4
5
6

print train_df.head(5)

print train_df.info()

print train_data.describe(include=["O"]).columns
查看object數(shù)據(jù)統(tǒng)計(jì)信息
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98

print train_df.describe()

print train_df.describe(include=["O"])

print train_df["Title"].value_counts()

train_df = train_df.drop(["Name", "PassengerId"], axis=1)
缺失值處理

print df4.dropna(axis=0,subset=["col1"]) # 丟棄nan的行,subset指定查看哪幾列
print df4.dropna(axis=1) # 丟棄nan的列

dataset["Cabin"] = dataset["Cabin"].fillna("U")
dataset["Title"] = dataset["Title"].fillna(0)

freq_port = train_df.Embarked.dropna().mode()[0]
dataset["Embarked"] = dataset["Embarked"].fillna(freq_port)

test_df["Fare"].fillna(test_df["Fare"].dropna().median(), inplace=True)
test_df["Fare"].fillna(test_df["Fare"].dropna().mean(), inplace=True)
數(shù)值屬性離散化，object屬性數(shù)值化

train_df["FareBand"] = pd.qcut(train_df["Fare"], 4)

train_df[["FareBand", "Survived"]].groupby(["FareBand"], as_index=False).mean().sort_values(by="FareBand", ascending=True)

title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Royalty":5, "Officer": 6}
dataset["Title"] = dataset["Title"].map(title_mapping)
計(jì)算特征與target屬性之間關(guān)系
object與連續(xù)target屬性之間，可以groupby均值
object與離散target屬性之間，先將target數(shù)值化，然后groupby均值，或者分別條形統(tǒng)計(jì)圖
連續(xù)屬性需要先切割然后再進(jìn)行g(shù)roupby計(jì)算，或者pearson相關(guān)系數(shù)
print train_df[["AgeBand", "Survived"]].groupby(["AgeBand"], as_index=False).mean().sort_values(by="AgeBand", ascending=True)
總結(jié)pandas基本操作
”"
創(chuàng)建df對象
””"
s1 = pd.Series([1,2,3,np.nan,4,5])
s2 = pd.Series([np.nan,1,2,3,4,5])
print s1
dates = pd.date_range(“20130101”,periods=6)
print dates
df = pd.DataFrame(np.random.rand(6,4),index=dates,columns=list(“ABCD”))

df2 = pd.DataFrame({“A”:1,
‘B":pd.Timestamp(‘20130102"),
‘C":pd.Series(1,index=list(range(4)),dtype="float32"),
‘D":np.array([3]*4,dtype=np.int32),
‘E":pd.Categorical([‘test","train","test","train"]),
‘F":"foo"
})

df3 = pd.DataFrame({"col1":s1,

 "col2":s2

})
print df3
"""
2.查看df數(shù)據(jù)
"""
print df3.head(2) #查看頭幾條
print df3.tail(3) #查看尾幾條
print df.index #查看索引
print df.info() #查看非non數(shù)據(jù)條數(shù)
print type(df.values) #返回二元數(shù)組

print df.describe() #對每列數(shù)據(jù)進(jìn)行初步的統(tǒng)計(jì)
print df3
print df3.sort_values(by=["col1"],axis=0,ascending=True) #按照哪幾列排序
"""
3.選擇數(shù)據(jù)
"""
ser_1 = df3["col1"]
print type(ser_1) #pandas.core.series.Series
print df3[0:2] #前三行
print df3.loc[df3.index[0]] #通過index來訪問
print df3.loc[df3.index[0],["col2"]] #通過行index，和列名來唯一確定一個(gè)位置
print df3.iloc[1] #通過位置來訪問
print df3.iloc[[1,2],1:2] #通過位置來訪問
print "==="
print df3.loc[:,["col1","col2"]].as_matrix() # 返回nunpy二元數(shù)組
print type(df3.loc[:,["col1","col2"]].as_matrix())
"""
4.布爾索引，過濾數(shù)據(jù)
"""
print df3[df3.col1 >2]
df4 = df3.copy()
df4["col3"]=pd.Series(["one","two","two","three","one","two"])
print df4
print df4[df4["col3"].isin(["one","two"])]
df4.loc[:,"col3"]="five"
print df4
"""
5.缺失值處理，pandas將缺失值用nan代替
"""
print pd.isnull(df4)
print df4.dropna(axis=0,subset=["col1"]) # 丟棄nan的行,subset指定查看哪幾列
print df4.dropna(axis=1) # 丟棄nan的列