pandas常用方法示例 2

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参考：https://mp.weixin.qq.com/s/Z1iNkI0ulstyNAeULKACFQfrom pandas import DataFrame
import numpy as np
import pandas as pd

t=
"age": [18, 30, np.nan, 40, np.nan, 30],
"city": ["BeiJing", "ShangHai", "GuangZhou", "ShenZhen", BeiJing, "ShangHai"],
"sex": [None, "male", "female", "male", np.nan, "unknown"],
"birth": ["2000-02-10", "1988-10-17", None, "1978-08-08", np.nan, "1988-10-17"],
"score":[11,7,33,44,22,33],
"naem":[sdf,aa,bb,tt,ere,tt],
"name":[sdf 11,aa 22,bb 33,tt 44,ere 55,tt 66]

df =DataFrame(t,index=[num1,num2,num3,num4,num5,num6])

将第1行和第2行的age,name返回：print(df.loc[df.index[[0,2]],[age,name]])

增加一行：df.loc[num]=[11,shandong,male,2020-02-02,11,sdf,sdf]

按sex进行分组，求每组age的平均值：print(df.groupby(sex)[age].mean())

对列求和、平均、最大

print(df[age].mean())
print(df[age].sum())
print(df[age].max())

按 age降序birth升序排：print(df.sort_values(by=[age, birth], ascending=[False, True]))

naem列中sdf替换成sss,ere替换成eee :print(df[naem].replace(["sdf","ere"],[sss,eee]))

print(df[age].replace(18,88))
读csv文件，指定编码，分隔符Separator：df = pd.read_csv(test.csv, encoding=gbk, sep=; )

d = "customer": ["A", "B", "C", "D"],
"sales":[1100, "950.5RMB", "$400", " $1250.75"]

df = pd.DataFrame(d)
print(df)
df["sales"] = df["sales"].replace("[$,RMB]", "", regex = True).astype("float") # 清洗掉 RMB
，$
符号，转化这一列为浮点型
print(df)

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)
print(df)
print(====合并多列为一列=======)
df = df.melt(
id_vars = "district_code",
var_name = "fruit_name",
value_name = "price")
print(df)

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(1,10,size=(48,3)),
columns = [商品编码,商品销量,商品库存])
df.index=pd.date_range(start=2020-03-01,periods=48,freq=H)
print(df)
#按天聚合，不用通过groupby实现
day_df = df.resample("D")["商品销量"].sum().to_frame()
print(day_df)

商品销量
2020-03-01122
2020-03-02129

不是按列值做分组，而是自定义分组
people = pd.DataFrame(np.random.randn(5, 5),
columns=[a, b, c, d, e],
index=[Joe, Steve, Wes, Jim, Travis])
mapping = a:red, b:red, c:blue,
d:blue, e:red, f:orange
by_column = people.groupby(mapping, axis=1)
print(by_column.sum())

一列拆成多列
df = pd.DataFrame(列1:[a,b,c],列2:[[10,20], [20,30], [30,40]])

df_new = df.列2.apply(pd.Series)

字符串拆成多列
df = pd.DataFrame(姓名:[张三,李四,王五],
所在地:[北京-东城区,上海-黄浦区,广州-白云区])
df.姓名.str.split( , expand=True)

根据出现频率筛选 DataFrame（挑选出现次数多的行）
t=
"age": [18, 30, 3, 30, np.nan, 30],
"city": ["BeiJing1", "ShangHai", "GuangZhou", "ShangHai", BeiJing, "ShangHai"],
"sex": [None, "male", "female", "male", np.nan, "unknown"],
"birth": ["2000-02-10", "1988-10-17", None, "1978-08-08", np.nan, "1988-10-17"],
"score":[7,7,33,44,7,33],
"naem":[sdf,aa,bb,aa,sdf,tt],
"name":[sdf 11,aa 22,bb 33,tt 44,ere 55,tt 66]

df =DataFrame(t)
c = (df[score].value_counts())
print(c.nlargest(1).index)
print(df[df.score.isin(c.nlargest(1).index)])

类型转换：
df = pd.DataFrame(列1:[1,2,3],
列2:[4.4,5.5,6.6],
列3:[7.7,8.8,-])

df2=df.astype(列1:int,列2:float)
print(df2.dtypes)
print(==========)
print(df.dtypes)

用这种方式转换第三列会出错，因为这列里包含一个代表 0 的下划线，pandas 无法自动判断这个下划线。为了解决这个问题，可以使用 to_numeric() 函数来处理第三列，让 pandas 把任意无效输入转为 NaN。
df = df.apply(pd.to_numeric, errors=coerce).fillna(0)

挑选指定类型的列：
print(df.select_dtypes(include=[number,datetime]))
排除指定类型的列：
print(df.select_dtypes(exclude=[number]))

多列合并为一行

df = pd.DataFrame(id_part:[a,b,c,d], pred:[0.1,0.2,0.3,0.4], pred_class:[women,man,cat,dog], v_id:[d1,d2,d3,d1])
print(df)
df2 = df.groupby([v_id]).agg(pred_class: [, .join],pred: lambda x: list(x),id_part: first).reset_index()
print(df2)

获取分组里最大值所在的行方法
df = pd.DataFrame(Sp:[a,b,c,d,e,f], Mt:[s1, s1, s2,s2,s2,s3], Value:[1,2,3,4,5,6], Count:[3,2,5,10,10,6])
df.iloc[df.groupby([Mt]).apply(lambda x: x[Count].idxmax())]

有重复值的情况
df["rank"] = df.groupby("ID")["score"].rank(method="min", ascending=False).astype(np.int64)
df[df["rank"] == 1][["ID", "class"]]

【pandas常用方法示例 2】