python - 将函数应用于数据框字典

Question

我有一个数据帧字典，Di_N。请问如何对每个数据框应用相同的功能？

数据帧的名称是根据数据生成的，因此代码中未定义这些名称。

以下代码已被编辑以使用 JPP 的答案； “迭代字典键并按顺序修改每个键的数据帧”:

import pandas as pd
import numpy  as np
import copy

# Data
df_1 = pd.DataFrame({'Box' : [1006,1006,1006,1006,1006,1006,1007,1007,1007,1007,1008,1008,1008,1009,1009,1010,1011,1011,1012,1013],
                     'Item': [  40,  41,  42,  43,  44,  45,  40,  43,  44,  45,  43,  44,  45,  40,  41,  40,  44,  45,  44,  45]})


df_Y = pd.DataFrame({'Box' : [1006,1007,1008,1009,1010,1011,1012,1013,1014],
                     'Type': [ 103, 101, 102, 102, 102, 103, 103, 103, 103]})

# Find whether each Box contains each Item
def is_number(s):
    try:
        float(s)
        return 1
    except ValueError:
        return 0
df_1['Thing'] = df_1['Item'].apply(is_number)

# Join
df_N = df_1.set_index('Box').join(df_Y.set_index('Box', 'outer')) # Why isn't Box 1014 in df_N?

# Find how many Boxes there are of each Type
def fun(g):
    try:
        return float(g.shape[0])
    except ZeroDivisionError:
        return np.nan
df_T = df_Y.groupby('Type').apply(fun).to_frame().transpose()

# Map of Box Type
Ma_G = df_N.groupby('Type')

# Group the Boxes by Type
Di_1 = {}
for name, group in Ma_G:
    Di_1[str(name)] = group

Di_2 = copy.deepcopy(Di_1)
Di_3 = {}

# Function to find the Mean of how many times each Item is in a Box
def fun(g):
    try:
        return float(g.shape[0])
    except ZeroDivisionError:
        return np.nan

numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']

for k in Di_1:

    # Table of which Item is in which Box
    Di_2[k] = pd.pivot_table(Di_1[k], values='Thing', columns='Item', index=['Box'], aggfunc=np.sum).fillna(0)

    # Find the Mean of how many times each Item is in a Box
    Di_3[k] = Di_1[k] .groupby('Item') .apply(fun)  .to_frame() .transpose()
    Di_3[k] = (Di_3[k].loc[0] / len(Di_1[k].index)) .to_frame() .transpose()

Di_4 = copy.deepcopy(Di_2)
for k in Di_1:

    # Compare each Box to the Mean - is this valid?
    Di_4[k] = pd.DataFrame(Di_2[k].values - Di_3[k].values, columns=Di_2[k].columns, index=Di_2[k].index)

    for c in [c for c in Di_4[k].columns if Di_4[k][c].dtype in numerics]:
        Di_4[k][c] = Di_4[k][c].abs()

    Di_2[k]['Unusualness'] = Di_4[k].sum(axis=1)

Answer 1

只需迭代字典键并按顺序修改每个键的数据帧即可。下面是一些伪代码来演示如何执行此操作:

for k in Di_N:
    Di_N[k] = pd.pivot_table(Di_N[k], values='Thing', ...).fillna(0)
    ....
    df_3 = ....
    df_4 = pd.DataFrame(Di_N[k].values - .... )
    Di_N[k]['Unusualness'] = df_4.sum(axis=1)

有一些位不需要包含在循环中，例如fun() 和 numerics 的定义。将它们放在循环之外，您仍然可以在循环内引用这些对象。

此外，您可以使用pd.DataFrame.select_dtypes选择数字列:

num_cols = df_4.select_dtypes(include=numerics).columns
df_4[num_cols] = df_4[num_cols].abs()