窗口函数rolling()
自建数据:
import pandas as pd
df = pd.DataFrame({"a": [10, 20, 10, 60, 40, 20, 50]})
df
输出:
下进行如下操作: b: 逐三行求和 c: 逐三行求和并放置在中间行(两行的中间行是靠下的那个) d: 逐三行求最大 e: 逐三行求最小 f: 逐三行求均值
df['b'] = df['a'].rolling(3).sum()
df['c'] = df['a'].rolling(3, center=True).sum()
df['d'] = df['a'].rolling(3).max()
df['e'] = df['a'].rolling(3).min()
df['f'] = df['a'].rolling(3).mean()
df
输出结果:
a | b | c | d | e | f |
---|
0 | 10 | NaN | NaN | NaN | NaN | 1 | 20 | NaN | 40.0 | NaN | NaN | 2 | 10 | 40.0 | 90.0 | 20.0 | 10.0 | 3 | 60 | 90.0 | 110.0 | 60.0 | 10.0 | 4 | 40 | 110.0 | 120.0 | 60.0 | 10.0 | 5 | 20 | 120.0 | 110.0 | 60.0 | 20.0 | 6 | 50 | 110.0 | NaN | 50.0 | 20.0 |
format string
避免重复书写的好帮手。
labels = ["{0} - {1}".format(i, i + 9) for i in range(0, 100, 10)]
labels
输出: [‘0 - 9’, ‘10 - 19’, ‘20 - 29’, ‘30 - 39’, ‘40 - 49’, ‘50 - 59’, ‘60 - 69’, ‘70 - 79’, ‘80 - 89’, ‘90 - 99’]
格式二:
[f'x is {x}' for x in range(10) ]
输出: [‘x is 0’, ‘x is 1’, ‘x is 2’, ‘x is 3’, ‘x is 4’, ‘x is 5’, ‘x is 6’, ‘x is 7’, ‘x is 8’, ‘x is 9’]
一个方框‘[]’是Series, 两个方框‘[[]]’是DataFrame
例如已有如下DataFrame,名字叫‘df’:
a | b | c | d | e | f |
---|
0 | 10 | NaN | NaN | NaN | NaN | 1 | 20 | NaN | 40.0 | NaN | NaN | 2 | 10 | 40.0 | 90.0 | 20.0 | 10.0 | 3 | 60 | 90.0 | 110.0 | 60.0 | 10.0 | 4 | 40 | 110.0 | 120.0 | 60.0 | 10.0 | 5 | 20 | 120.0 | 110.0 | 60.0 | 20.0 | 6 | 50 | 110.0 | NaN | 50.0 | 20.0 |
取a列:
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一个‘[]’:  -
两个‘[[]]’: 
某个结构后面想用函数可以按‘ tab’键
DadaFrame中取值一般是‘[]’,取函数一般是‘()’
存文件可以考虑pickle或者parquet
 
它是存成二进制文件,读写比csv快。
Catogorical 类别变量
- 无序的
不用举例了吧,猫 啊,狗的,无序。 - 有序的
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分箱操作
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cut()   -
qcut() 
DataFrame自带画图方法,无需导入seaborn或matplotlib
 还可以是这些: 
apply()
- Series的apply()
 - DataFrame的apply()
 axis=0是index间操作,axis=1是columns间操作 
每列都统计value_counts()

按某列排序

DataFrame迭代
- 按列迭代
 - 按行迭代
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pivot()
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pivot_table()
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
crosstab()
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正则表达式
df['url'] = df['url'].apply(lambda x: re.sub(':4443',':4442',x))
apply默认是行间的操作,这里把每行url列的3替换成了2
待更新:时间模块 datetime()
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