Pandas数据分析技巧 Pandas数据分析的一些常用小技巧
北山啦 人气:0Pandas小技巧
import pandas as pd
pandas生成数据
d = {"sex": ["male", "female", "male", "female"],
"color": ["red", "green", "blue", "yellow"],
"age": [12, 56, 21, 31]}
df = pd.DataFrame(d)
df
| sex | color | age | |
|---|---|---|---|
| 0 | male | red | 12 |
| 1 | female | green | 56 |
| 2 | male | blue | 21 |
| 3 | female | yellow | 31 |
数据替换–map映射
map() 会根据提供的函数对指定序列做映射。
map(function, iterable, …)
- function – 函数
- iterable – 一个或多个序列
d = {"male": 1, "female": 0}
df["gender"] = df["sex"].map(d)
df
| sex | color | age | gender | |
|---|---|---|---|---|
| 0 | male | red | 12 | 1 |
| 1 | female | green | 56 | 0 |
| 2 | male | blue | 21 | 1 |
| 3 | female | yellow | 31 | 0 |
数据清洗–replace和正则
分享pandas数据清洗技巧，在某列山使用replace和正则快速完成值的清洗
d = {"customer": ["A", "B", "C", "D"],
"sales": [1000, "950.5RMB", "$400", "$1250.75"]}
df = pd.DataFrame(d)
df
| customer | sales | |
|---|---|---|
| 0 | A | 1000 |
| 1 | B | 950.5RMB |
| 2 | C | $400 |
| 3 | D | $1250.75 |
sales列的数据类型不同意，为后续分析，所以需要将他的格式同统一
df["sales"] = df["sales"].replace("[$,RMB]", "", regex=True).astype("float")
df
| customer | sales | |
|---|---|---|
| 0 | A | 1000.00 |
| 1 | B | 950.50 |
| 2 | C | 400.00 |
| 3 | D | 1250.75 |
查看数据类型
df["sales"].apply(type)
0 <class 'float'>
1 <class 'float'>
2 <class 'float'>
3 <class 'float'>
Name: sales, dtype: object
数据透视表分析–melt函数
melt是逆转操作函数，可以将列名转换为列数据(columns name → column values)，重构DataFrame，用法如下：
参数说明：
pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=‘value', col_level=None)
- frame:要处理的数据集。
- id_vars:不需要被转换的列名。
- value_vars:需要转换的列名，如果剩下的列全部都要转换，就不用写了。
- var_name和value_name是自定义设置对应的列名。
- col_level :如果列是MultiIndex，则使用此级别。
二维表格转成一维表格
d = {"district_code": [12345, 56789, 101112, 131415],
"apple": [5.2, 2.4, 4.2, 3.6],
"banana": [3.5, 1.9, 4.0, 2.3],
"orange": [8.0, 7.5, 6.4, 3.9]
}
df = pd.DataFrame(d)
df
| district_code | apple | banana | orange | |
|---|---|---|---|---|
| 0 | 12345 | 5.2 | 3.5 | 8.0 |
| 1 | 56789 | 2.4 | 1.9 | 7.5 |
| 2 | 101112 | 4.2 | 4.0 | 6.4 |
| 3 | 131415 | 3.6 | 2.3 | 3.9 |
df = df.melt(id_vars="district_code", var_name="fruit_name", value_name="price") df
| district_code | fruit_name | price | |
|---|---|---|---|
| 0 | 12345 | apple | 5.2 |
| 1 | 56789 | apple | 2.4 |
| 2 | 101112 | apple | 4.2 |
| 3 | 131415 | apple | 3.6 |
| 4 | 12345 | banana | 3.5 |
| 5 | 56789 | banana | 1.9 |
| 6 | 101112 | banana | 4.0 |
| 7 | 131415 | banana | 2.3 |
| 8 | 12345 | orange | 8.0 |
| 9 | 56789 | orange | 7.5 |
| 10 | 101112 | orange | 6.4 |
| 11 | 131415 | orange | 3.9 |
将分类中出现次数较少的值归为others
d = {"name": ['Jone', 'Alica', 'Emily', 'Robert', 'Tomas',
'Zhang', 'Liu', 'Wang', 'Jack', 'Wsx', 'Guo'],
"categories": ["A", "C", "A", "D", "A",
"B", "B", "C", "A", "E", "F"]}
df = pd.DataFrame(d)
df
| name | categories | |
|---|---|---|
| 0 | Jone | A |
| 1 | Alica | C |
| 2 | Emily | A |
| 3 | Robert | D |
| 4 | Tomas | A |
| 5 | Zhang | B |
| 6 | Liu | B |
| 7 | Wang | C |
| 8 | Jack | A |
| 9 | Wsx | E |
| 10 | Guo | F |
D、E、F 仅在分类中出现一次，A 出现次数较多。
统计出现次数，并标准化
frequencies = df["categories"].value_counts(normalize=True) frequencies
A 0.363636
B 0.181818
C 0.181818
E 0.090909
D 0.090909
F 0.090909
Name: categories, dtype: float64
设定阈值
threshold = 0.1 small_categories = frequencies[frequencies < threshold].index small_categories
Index(['E', 'D', 'F'], dtype='object')
替换
df["categories"] = df["categories"].replace(small_categories, "Others")
df
| name | categories | |
|---|---|---|
| 0 | Jone | A |
| 1 | Alica | C |
| 2 | Emily | A |
| 3 | Robert | Others |
| 4 | Tomas | A |
| 5 | Zhang | B |
| 6 | Liu | B |
| 7 | Wang | C |
| 8 | Jack | A |
| 9 | Wsx | Others |
| 10 | Guo | Others |
Python小技巧
列表推导式
例如，假设我们想创建一个正方形列表，例如
squares = [] for x in range(10): squares.append(x**2) squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
squares = list(map(lambda x: x**2, range(10))) squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
squares = [x**2 for x in range(10)] squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
同时还可以利用if来过滤列表
[(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
列表推导式可以包含复杂表达式和嵌套函数
from math import pi [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
列表推导式中的初始表达式可以是任意表达式，包括另一个列表推导式。
下面的列表推导式将对行和列进行转置
matrix = [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], ]
[[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
交换变量
a = 1
b = 2
a, b = b, a
print("a = ",a)
print("b = ",b)
a = 2
b = 1
检查对象使用内存情况
sys.getsizeof()
range()函数返回的是一个类，在使用内存方面，range远比实际的数字列表更加高效
import sys mylist = range(1,10000) print(sys.getsizeof(mylist))
48
合并字典
从Python3.5开始，合并字典的操作更加简单
如果key重复，那么第一个字典的key会被覆盖
d1 ={"a":1,"b":2}
d2 = {"b":2,"c":4}
m = {**d1,**d2}
print(m)
{'a': 1, 'b': 2, 'c': 4}
字符串分割成列表
string = "the author is beishanla"
s = string.split(" ")
s
['the', 'author', 'is', 'beishanla']
字符串列表创建字符串
l = ["the","author","is","beishanla"] l = " ".join(l) l
'the author is beishanla'
Python查看图片
pip install Pillow
from PIL import Image
im = Image.open("E:/Python/00网络爬虫/Project/词云图跳舞视频/aip-python-sdk-4.15.1/pictures/img_88.jpg")
im.show()
print(im.format,im.size,im.mode)
JPEG (1920, 1080) RGB
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