Python使用dict列在dataframe中追加/更新键值对

Question

拥有这个数据帧

d = {'objects':[{'Sand':10},{'Seawater': 2, 'Crab': 30},{'Parasol': 50}]}
df = pd.DataFrame(data=d)

我想要这个键值对

{'Small': 1000}

插入到至少有一个值小于40的键值对的每一行

     objects
0    {'Sand': 10, 'Small': 1000}
1    {'Seawater': 2, 'Crab': 30, 'Small': 1000}
2    {'Parasol': 50}

我试过在上面循环，但结果是“没有”

def your_small(x):
    if any(value < 40 for value in x.values()):
        return x.update({'Small': 1000})

d = {'objects':[{'Sand':10},{'Seawater': 2, 'Crab': 30},{'Parasol': 50}]}
df = pd.DataFrame(data=d)
df['objects'] = df['objects'].map(your_small)

  objects
0    None
1    None
2    None

Answer 1

如果dicts不是真的需要，这里有一个多索引的方法。在这里，我假设单独的dict大部分都是不重叠的键，所以长数据帧似乎更合适(如果大多数dict都有重叠的键，那么宽数据帧可能更好）

import pandas as pd

df = pd.concat([pd.DataFrame.from_dict(di, orient='index', columns=['N']) for di in d['objects']], 
               keys=range(len(d['objects'])))
#             N
#0 Sand      10
#1 Seawater   2
#  Crab      30
#2 Parasol   50

# Determine which original "rows" have at least one value < 40
s = df.N.lt(40).groupby(level=0).any()

df_add = pd.DataFrame({'N': 1000},
                      index = pd.MultiIndex.from_product([s[s].index, ['Small']]))

# Join them:
df = pd.concat([df, df_add]).sort_index()
#               N
#0 Sand        10
#  Small     1000
#1 Crab        30
#  Seawater     2
#  Small     1000
#2 Parasol     50

---

这是一个具有宽数据帧的版本。更容易操作，但可以变得非常大

df = pd.DataFrame.from_records(d['objects'])
#   Sand  Seawater  Crab  Parasol
#0  10.0       NaN   NaN      NaN
#1   NaN       2.0  30.0      NaN
#2   NaN       NaN   NaN     50.0

df.loc[df.lt(40).any(1), 'Small'] = 1000
#   Sand  Seawater  Crab  Parasol   Small
#0  10.0       NaN   NaN      NaN  1000.0
#1   NaN       2.0  30.0      NaN  1000.0
#2   NaN       NaN   NaN     50.0     NaN

Answer 2

正如@ALoll所说，您可能需要重新考虑您的方法

如果要使现有代码正常工作，必须考虑map的工作方式：必须在map函数中返回一个值x.update返回None，如果不满足条件，则必须按原样返回x：

def your_small(x):
    if any(value < 40 for value in x.values()):
        return {**x, 'Small': 1000}
    return x