如何在 Python 中使用 for 循环对 DataFrame 进行子集化和列出？

Question

我有一个包含 3 列和 1,000 多行的 DataFrame，

df 
   day         product         order
2010-01-01    150ml Mask          9
2010-01-02    230ml Lotion       27
2010-01-03    600ml Shampoo      33

我想将每个产品子集如下，

 df_mask                 df_lotion            df_shampoo  
   day        order        day       order     day         order
2010-01-01      9       2010-01-02    27      2010-01-03    33   
2010-01-09      8       2010-01-05    30      2010-01-04    25
2010-01-11     13       2010-01-06    29      2010-01-06    46

我就是这么干的，

# Create a product list 
productName = df['product'].tolist()

# Subsetting
def subtable(df,productName):
    return (df[(df['product'] == productName)])

# Subsetting
df_mask = subtable(df, '150ml Mask')
df_lotion = subtable(df, '230ml Lotion')
df_shampoo = subtable(df, '230ml Shampoo')

由于数据框有许多不同的产品，有什么方法可以让我一次使用 for 循环获取所有子集。

Answer 1

我认为您可以使用dict 存储所有DataFrames，它是由dict comprehension 与groupby 和split 创建的：

producs = df['product'].str.split().str[-1]
print (producs)
0       Mask
1     Lotion
2    Shampoo
Name: product, dtype: object

dfs = {i:df.reset_index(drop=True) for i, df in df.groupby(producs)}
print (dfs)
{'Shampoo':           day        product  order
0  2010-01-03  600ml Shampoo     33, 'Mask':           day     product  order
0  2010-01-01  150ml Mask      9, 'Lotion':           day       product  order
0  2010-01-02  230ml Lotion     27}

print (dfs['Shampoo'])
          day        product  order
0  2010-01-03  600ml Shampoo     33

如果您需要删除列product，请使用子集[['day','order']] 或drop：

dfs = {i:df.reset_index(drop=True)[['day','order']] for i, df in df.groupby(producs)}
#dfs = {i:df.reset_index(drop=True).drop('product', axis=1) for i, df in df.groupby(producs)}
print (dfs)
{'Shampoo':           day  order
0  2010-01-03     33, 'Mask':           day  order
0  2010-01-01      9, 'Lotion':           day  order
0  2010-01-02     27}

print (dfs['Shampoo'])
          day  order
0  2010-01-03     33

Answer 2

您可以为此使用groupby，这正是您所需要的：

# show example data
print(df)

     day           product             order
0    2010-01-01    "150ml Mask"          9
1    2010-01-02    "230ml Lotion"       27
2    2010-01-03    "600ml Shampoo"      33
3    2010-01-04    "250ml Mask"         12
4    2010-01-05    "330ml Lotion"       24
5    2010-01-06    "400ml Shampoo"      13

# split product column and keep only product name
df["product"] = df["product"].str.split(expand=True)[1]

# groupby product
products = df.groupby("product")

# print product and corresponding product df
for product, product_df in products:
    print(product)
    print(product_df)

Lotion
          day product  order
1  2010-01-02  Lotion     27
4  2010-01-05  Lotion     24

Mask
          day product  order
0  2010-01-01    Mask      9
3  2010-01-04    Mask     12

Shampoo
          day  product  order
2  2010-01-03  Shampoo     33
5  2010-01-06  Shampoo     13

为了单独访问每个子组，您可以使用与您的subtable 函数相对应的get_group：

mask_df = products.get_group("Mask")
print(mask_df)

    day         product     order
0   2010-01-01  Mask        9
3   2010-01-04  Mask        12

最后，要获取一个字典中的所有子数据帧，您可以循环 products 并删除产品列本身：

df_dict = {product: product_df.drop("product", axis=1) 
          for product, product_df in products}
print(df_dict["Mask"])

    day         order
0   2010-01-01  9
3   2010-01-04  12

Answer 3

看看有没有帮助：

dfs = {}
for grp in df.groupby('product'):
    dfs[grp[0].split(' ')[1]] = grp[1] # split gives you the product name as key

for key in dfs.keys():
    print dfs[key]