Python——pandas的使用

Pandas的經常使用數據類型

• Series一維，帶標籤的數組
• DataFrame二維，Series容器

1、Series

Series對象本質上是有兩個數組組成，一個數組構成對象的鍵（index），一個數組構成對象的值（values）

import string

import pandas as pd
import numpy as np


# 建立Series
t1 = pd.Series(np.arange(5),index=list("abcde"))

print(t1)
"""
索引能夠指定，默認爲012...

a    0
b    1
c    2
d    3
e    4
dtype: int64
"""
print(type(t1)) # <class 'pandas.core.series.Series'>


# 使用字典建立Series

a = {string.ascii_uppercase[i]:i for i in range(5)}

# 建立Series
print(pd.Series(a))
"""
A    0
B    1
C    2
D    3
E    4
dtype: int64
"""

print(pd.Series(a,index=list("CDEFG")))
"""
C    2.0
D    3.0
E    4.0
F    NaN
G    NaN
dtype: float64
"""


# 切片

print(t1[0:4:2])
"""
a    0
c    2
dtype: int64
"""

print(t1[[2,3,4]])
"""
c    2
d    3
e    4
dtype: int64
"""

print(t1[t1>2])
"""
d    3
e    4
dtype: int64
"""

print(t1["b"])  # 1


print(t1[["a","e","f"]])
"""
a    0.0
e    4.0
f    NaN
dtype: float64
"""

# 索引和值

print(t1.index)     # Index(['a', 'b', 'c', 'd', 'e'], dtype='object')
print(type(t1.index))   # <class 'pandas.core.indexes.base.Index'>

print(t1.values)    # [0 1 2 3 4]
print(type(t1.values))  # <class 'numpy.ndarray'>

2、DataFrame

建立DataFrame

# 建立DataFrame對象

t1 = pd.DataFrame(np.arange(12).reshape(3,4))

print(t1)
"""
DataFrame對象既有行索引，又有列索引
行索引，代表不一樣行，橫向索引，叫index，0軸，axis=0
列索引，表名不一樣列，縱向索引，叫columns，1軸，axis=1

   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
"""

t2 = pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("EFGH"))

print(t2)
"""
   E  F   G   H
a  0  1   2   3
b  4  5   6   7
c  8  9  10  11
"""

# 將字典轉換成dataframe
temp_dict = [{"name":"zhangsan","age":15,"tel":10086},
             {"name":"lisi","age":15},
             {"name":"wangwu","tel":10086}
             ]

t3 = pd.DataFrame(temp_dict)
print(t3)
"""
    age      name      tel
0  15.0  zhangsan  10086.0
1  15.0      lisi      NaN
2   NaN    wangwu  10086.0
"""

獲取DataFrame的基本信息

# 獲取DataFrame的基本信息

# 行數，列數
print(t1.shape)

# 列數據類型
print(t1.dtypes)

# 數據維度
print(t1.ndim)  # 2
# 行索引
print(t1.index) # RangeIndex(start=0, stop=3, step=1)
# 列索引
print(t2.columns)   # Index(['E', 'F', 'G', 'H'], dtype='object')
# 對象值
print(t1.values)
"""
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
"""
# 顯示頭幾行，默認是5
print(t1.head(2))
# 顯示末尾幾行
print(t1.tail(2))
# 相關信息概覽：行數，列數，列索引，咧非空值個數，行列類型，內存佔用
print(t1.info())
"""
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
0    3 non-null int64
1    3 non-null int64
2    3 non-null int64
3    3 non-null int64
dtypes: int64(4)
memory usage: 176.0 bytes
None
"""
# 快速綜合統計結果：計數，均值，標準差，最大值，1/4值，最小值
print(t2.describe())
"""
是根據列來計算的
         E    F     G     H
count  3.0  3.0   3.0   3.0
mean   4.0  5.0   6.0   7.0
std    4.0  4.0   4.0   4.0
min    0.0  1.0   2.0   3.0
25%    2.0  3.0   4.0   5.0
50%    4.0  5.0   6.0   7.0
75%    6.0  7.0   8.0   9.0
max    8.0  9.0  10.0  11.0
"""

加載csv數據

import numpy as np
import pandas as pd

# 加載csv數據
t = pd.read_csv("./dogNames2.csv")

# 按照字段進行排序，ascending   desc/asc
t2 = t.sort_values("Count_AnimalName",ascending=False).head(10)

print(t2)

獲取行列數據

import string

import numpy as np
import pandas as pd


# loc和iloc方法
    # df.loc：經過標籤獲取行數據
    # df.iloc：經過位置獲取行數據

t1 = pd.DataFrame(np.arange(12).reshape(3,4),index=list("abc"),columns=list("EFGH"))

# 獲取行列交叉部分
print(t1.loc["a","E"])  # 0

# 獲取行與多列交叉部分
print(t1.loc["a",["E","F"]])
"""
E    0
F    1
"""

# 獲取行與多列交叉部分
print(t1.loc["a","E":"G"])
"""
E    0
F    1
G    2
"""

# 獲取行與連續多列交叉部分
print(t1.loc["a":"c","G"])
"""
注意：loc裏的:是包括最後的那個的
a     2
b     6
c    10
"""

# iloc和loc是同樣的，只不過採用的是索引來進行的操做

布爾索引

import numpy as np
import pandas as pd


# pandas中的布爾索引
t1 = pd.read_csv("./dogNames2.csv")

# 找出其中名字使用次數超過800的狗

print(t1[t1["Count_AnimalName"]>800])
"""
      Row_Labels  Count_AnimalName
1156       BELLA              1195
2660     CHARLIE               856
3251        COCO               852
9140         MAX              1153
12368      ROCKY               823
"""

# 找出狗名字符串長度超過4的狗

print(t1[t1["Row_Labels"].str.len()>4].head(3))
"""
  Row_Labels  Count_AnimalName
2      40804                 1
3      90201                 1
4      90203                 1
"""

# 多條件，要使用()分割，&或|作鏈接符
print(t1[(t1["Row_Labels"].str.len()>4)&(t1["Row_Labels"].str.len()<6)].head(3))

字符串方法

處理缺失數據

import pandas as pd
import numpy as np


temp_dict = [{"name":"zhangsan","age":0,"tel":10086},
             {"name":"lisi","age":15},
             {"name":"wangwu","tel":10010}]

t1 = pd.DataFrame(temp_dict)
print(t1)
"""
    age      name      tel
0  15.0  zhangsan  10086.0
1  15.0      lisi      NaN
2   NaN    wangwu  10010.0
"""

## 缺失數據的處理

#   - 處理方式1：刪除NaN所在的行列dropna (axis=0, how='any', inplace=False)
t2 = t1.dropna(axis=0, how='any', inplace=False)
print(t2)
"""
    age      name      tel
0  15.0  zhangsan  10086.0
"""
#   - 處理方式2：填充數據，t.fillna(t.mean()),t.fiallna(t.median()),t.fillna(0)
t3 = t1.fillna(t1.mean())
print(t3)
"""
    age      name      tel
0  15.0  zhangsan  10086.0
1  15.0      lisi  10048.0
2  15.0    wangwu  10010.0
"""

### 處理爲0的數據：將0改成nan，而後使用上面的方法進行填充
t1[t1==0] = np.nan

print(t1)


### 查看是否爲nan，返回布爾索引

print(pd.isnull(t1))
"""
     age   name    tel
0   True  False  False
1  False  False   True
2   True  False  False
"""
print(pd.notnull(t1))
"""
     age  name    tel
0  False  True   True
1   True  True  False
2  False  True   True
"""