import pandas as pd
import numpy as np
# 方式1：從列表創(chuàng)建（默認(rèn)整數(shù)索引 0,1,2...）
scores = [85, 92, 78, 90, 88]
s1 = pd.Series(scores)
print("從列表創(chuàng)建的Series：")
print(s1)
# 方式2：指定自定義索引
names = ["小明", "小紅", "小剛", "小麗", "小強(qiáng)"]
s2 = pd.Series(scores, index=names)
print("\n指定索引的Series：")
print(s2)
# 方式3：從NumPy數(shù)組創(chuàng)建
arr = np.array([15.2, 16.1, 14.8, 15.5])
s3 = pd.Series(arr, index=["1月", "2月", "3月", "4月"], name="月均溫度")
print("\n帶名稱的Series：")
print(s3)

import pandas as pd
# 字典：鍵=姓名，值=分?jǐn)?shù)
score_dict = {"小明":85, "小紅":92, "小剛":78, "小麗":90}
s4 = pd.Series(score_dict)
print("從字典創(chuàng)建的Series：")
print(s4)

import pandas as pd
import numpy as np
# 含NaN的Series（缺失值）
s5 = pd.Series([85, np.nan, 92, 78, np.nan], index=["小明", "小紅", "小剛", "小麗", "小強(qiáng)"])
print("含缺失值的Series：")
print(s5)

import pandas as pd
s = pd.Series([85, 92, 78, 90], index=["小明", "小紅", "小剛", "小麗"])
# 取值：通過標(biāo)簽
print("小紅的分?jǐn)?shù)：", s["小紅"])  # 92
# 賦值：通過標(biāo)簽
s["小剛"] = 80
print("\n修改小剛分?jǐn)?shù)后：")
print(s)
# 多標(biāo)簽取值（返回新Series）
print("\n小明和小麗的分?jǐn)?shù)：")
print(s[["小明", "小麗"]])

import pandas as pd
s = pd.Series([85, 92, 78, 90], index=["小明", "小紅", "小剛", "小麗"])
# 直接用整數(shù)（簡易寫法）
print("第2個(gè)元素（位置1）：", s[1])  # 92
# 用iloc（明確位置索引，推薦）
print("第4個(gè)元素（位置3）：", s.iloc[3])  # 90
# 位置切片
print("\n位置0-2的元素：")
print(s.iloc[0:3])

import pandas as pd
s = pd.Series([85, 92, 78, 90, 88], index=["小明", "小紅", "小剛", "小麗", "小強(qiáng)"])
# 篩選分?jǐn)?shù)≥90的元素
high_score = s[s >= 90]
print("90分以上的學(xué)生：")
print(high_score)
# 篩選分?jǐn)?shù)在80-90之間的元素
mid_score = s[(s >= 80) & (s < 90)]
print("\n80-90分的學(xué)生：")
print(mid_score)

import pandas as pd
import numpy as np
s = pd.Series([85, np.nan, 92, 78], index=["小明", "小紅", "小剛", "小麗"], name="分?jǐn)?shù)")
print("底層數(shù)組：", s.values)
print("索引：", s.index)
print("數(shù)據(jù)類型：", s.dtype)
print("形狀：", s.shape)
print("名稱：", s.name)
print("缺失值判斷：")
print(s.isnull())

import pandas as pd
import numpy as np
s = pd.Series([85, np.nan, 92, 78, 90], index=["小明", "小紅", "小剛", "小麗", "小強(qiáng)"])
# 基礎(chǔ)統(tǒng)計(jì)
print("均值（自動(dòng)忽略NaN）：", s.mean())  # 86.25
print("中位數(shù)：", s.median())            # 87.5
print("最大值/最小值：", s.max(), "/", s.min())  # 92 / 78
print("標(biāo)準(zhǔn)差：", s.std())              # 6.18...
# 描述性統(tǒng)計(jì)（一鍵生成）
print("\n描述性統(tǒng)計(jì)：")
print(s.describe())

import pandas as pd
import numpy as np
s = pd.Series([85, np.nan, 92, 78, np.nan], index=["小明", "小紅", "小剛", "小麗", "小強(qiáng)"])
# 1. 刪除缺失值
s_drop = s.dropna()
print("刪除缺失值后：")
print(s_drop)
# 2. 填充缺失值（均值/中位數(shù)/固定值）
s_fill_mean = s.fillna(s.mean())  # 均值填充
s_fill_0 = s.fillna(0)            # 固定值填充
print("\n均值填充缺失值：")
print(s_fill_mean)

import pandas as pd
s = pd.Series([85, 92, 78, 90], index=["小明", "小紅", "小剛", "小麗"])
# 按值升序排序
s_sort_val = s.sort_values()
print("按分?jǐn)?shù)升序：")
print(s_sort_val)
# 按索引降序排序
s_sort_idx = s.sort_index(ascending=False)
print("\n按姓名降序：")
print(s_sort_idx)

import pandas as pd
s = pd.Series([85, 92, 78, 59, 90], index=["小明", "小紅", "小剛", "小亮", "小麗"])
# 替換單個(gè)值（不及格→60）
s_replace = s.replace(59, 60)
print("替換不及格分?jǐn)?shù)后：")
print(s_replace)
# 映射（分?jǐn)?shù)→等級(jí)）
grade_map = {x: "優(yōu)秀" if x>=90 else "良好" if x>=80 else "及格" for x in s}
s_grade = s.map(grade_map)
print("\n分?jǐn)?shù)映射為等級(jí)：")
print(s_grade)

import pandas as pd
import numpy as np
# 1. Series → NumPy數(shù)組
s = pd.Series([85, 92, 78, 90], index=["小明", "小紅", "小剛", "小麗"])
arr = s.values
print("Series轉(zhuǎn)數(shù)組：", arr)  # [85 92 78 90]
# 2. Series → 列表
lst = s.tolist()
print("Series轉(zhuǎn)列表：", lst)  # [85, 92, 78, 90]
# 3. NumPy數(shù)組 → Series
arr2 = np.array([15.2, 16.1, 14.8])
s2 = pd.Series(arr2, index=["1月", "2月", "3月"])
print("\n數(shù)組轉(zhuǎn)Series：")
print(s2)
# 4. 列表 → Series
lst2 = [88, 76, 95]
s3 = pd.Series(lst2, index=["小強(qiáng)", "小芳", "小偉"])
print("\n列表轉(zhuǎn)Series：")
print(s3)

import pandas as pd
import numpy as np
# 創(chuàng)建日期索引的Series（7天溫度）
dates = pd.date_range(start="2026-01-01", periods=7)
temp = pd.Series(np.random.normal(5, 3, 7).round(1), index=dates, name="日溫")
# 查看數(shù)據(jù)
print("7天溫度數(shù)據(jù)：")
print(temp)
# 篩選1月3日之后的溫度
print("\n1月3日之后的溫度：")
print(temp[temp.index >= "2026-01-03"])
# 計(jì)算周平均溫度
print("\n周平均溫度：", temp.mean())

import pandas as pd
import numpy as np
# 模擬學(xué)生分?jǐn)?shù)（含缺失值）
scores = pd.Series([85, np.nan, 92, 78, np.nan, 88, 95], 
                   index=["小明", "小紅", "小剛", "小麗", "小強(qiáng)", "小芳", "小偉"])
# 步驟1：統(tǒng)計(jì)缺失值數(shù)量
nan_count = scores.isnull().sum()
print("缺失值數(shù)量：", nan_count)
# 步驟2：用中位數(shù)填充缺失值
scores_filled = scores.fillna(scores.median())
print("\n填充后的分?jǐn)?shù)：")
print(scores_filled)
# 步驟3：篩選優(yōu)秀學(xué)生（≥90）
excellent = scores_filled[scores_filled >= 90]
print("\n優(yōu)秀學(xué)生：")
print(excellent)