# coding: utf-8

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# 导入相关库
import numpy as np
import pandas as pd


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index = pd.Index(data=["Tom", "Bob", "Mary", "James"], name="name")

data = {
    "age": [18, 30, 25, 40],
    "city": ["BeiJing", "ShangHai", "GuangZhou", "ShenZhen"],
    "sex": ["male", "male", "female", "male"]
}

user_info = pd.DataFrame(data=data, index=index)
user_info


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user_info.info()


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user_info.shape


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user_info.T


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user_info


# # 如果我们想要通过 DataFrame 来获取它包含的原有数据，可以通过 .values 来获取，获取后的数据类型其实是一个 ndarray。

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user_info.values


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user_info.age.max()


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user_info.describe()


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user_info.sex.value_counts()


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#pd.cut(user_info.age, 3)


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pd.cut(user_info.age, [1, 18, 30, 50], labels=["childhood", "youth", "middle"])


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user_info


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user_info.sort_index(axis=1, ascending=False)


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user_info.age.map(lambda x: "yes" if x >= 30 else "no")


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city_map = {
    "BeiJing": "north",
    "ShangHai": "south",
    "GuangZhou": "south",
    "ShenZhen": "south"
}

# 传入一个 map
user_info.city.map(city_map)


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user_info


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user_info.rename(columns={"age": "Age", "city": "City", "sex": "Sex"})


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user_info.rename(index={"Tom": "tom", "Bob": "bob"})


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user_info.get_dtype_counts()


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user_info["age"].astype(float)
user_info


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user_info.to_csv("data.csv",sep=" ")


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