import pandas as pd
 
my_list = [10, 20, 30]
df = pd.DataFrame({'Values': my_list})  # 方法1：字典包裹
df = pd.DataFrame([my_list])            # 方法2：列表包裹

# 自定義索引名稱(chēng)
df = pd.DataFrame({'Values': my_list}, index=['A', 'B', 'C'])
 
# 重置索引
df.reset_index(inplace=True)
df.columns = ['ID', 'Values']  # 重命名列

s = pd.Series(my_list, name='Values')
df = s.to_frame()  # 自動(dòng)生成DataFrame

# 設(shè)置多級(jí)索引
s.index = pd.MultiIndex.from_tuples([(1, 'A'), (1, 'B'), (2, 'C')])
df = s.to_frame()
 
# 時(shí)間序列處理
dates = pd.date_range('20230101', periods=3)
s = pd.Series(my_list, index=dates)
df = s.to_frame().reset_index()
df.columns = ['Date', 'Value']

# 強(qiáng)制類(lèi)型轉(zhuǎn)換
df['Value'] = df['Value'].astype(float)
 
# 分類(lèi)數(shù)據(jù)轉(zhuǎn)換
df['Category'] = pd.Categorical(df['Value'], categories=[10,20,30])

nested_list = [[1,2], [3,4], [5,6]]
 
# 方法1：列表推導(dǎo)式展開(kāi)
df = pd.DataFrame({
    'Col1': [x[0] for x in nested_list],
    'Col2': [x[1] for x in nested_list]
})
 
# 方法2：apply函數(shù)處理
df = pd.DataFrame(nested_list).add_prefix('Col_')

dict_list = [{'A':1, 'B':2}, {'A':3, 'B':4}]
df = pd.DataFrame(dict_list)
 
# 處理缺失鍵
from functools import partial
merge = partial(dict.update, {'A':0, 'B':0})
clean_list = [merge(d) or d for d in dict_list]
df = pd.DataFrame(clean_list)

class DataPoint:
    def __init__(self, x, y):
        self.x = x
        self.y = y
 
obj_list = [DataPoint(1,2), DataPoint(3,4)]
df = pd.DataFrame([(o.x, o.y) for o in obj_list], columns=['X', 'Y'])

# 預(yù)分配空DataFrame
df = pd.DataFrame(index=range(len(my_list)), columns=['Values'])
df['Values'] = my_list

chunk_size = 100000
chunks = [my_list[i:i+chunk_size] for i in range(0, len(my_list), chunk_size)]
dfs = [pd.DataFrame(chunk, columns=['Values']) for chunk in chunks]
final_df = pd.concat(dfs, ignore_index=True)

# 轉(zhuǎn)換數(shù)值類(lèi)型
df['Values'] = pd.to_numeric(df['Values'], downcast='integer')
 
# 轉(zhuǎn)換分類(lèi)類(lèi)型
df['Category'] = pd.to_categorical(df['Values'])