在保持列数据类型的同时，将新行插入现有 pandas DataFrame 的最佳方法是什么，同时为未指定的列提供用户定义的填充值?这是一个例子:
What's the best way to insert new rows into an existing pandas DataFrame while maintaining column data types and, at the same time, giving user-defined fill values for columns that aren't specified? Here's an example:
df = pd.DataFrame({
'name': ['Bob', 'Sue', 'Tom'],
'age': [45, 40, 10],
'weight': [143.2, 130.2, 34.9],
'has_children': [True, True, False]
})
假设我想添加一条只传递 name 和 age 的新记录.为了维护数据类型，我可以从 df 复制行，修改值，然后将 df 附加到副本，例如
Assume that I want to add a new record passing just name and age. To maintain data types, I can copy rows from df, modify values and then append df to the copy, e.g.
columns = ('name', 'age')
copy_df = df.loc[0:0, columns].copy()
copy_df.loc[0, columns] = 'Cindy', 42
new_df = copy_df.append(df, sort=False).reset_index(drop=True)
但这会将 bool 列转换为对象.
But that converts the bool column to an object.
这是一个非常老套的解决方案，感觉不是这样做的正确方法":
Here's a really hacky solution that doesn't feel like the "right way" to do this:
columns = ('name', 'age')
copy_df = df.loc[0:0].copy()
missing_remap = {
'int64': 0,
'float64': 0.0,
'bool': False,
'object': ''
}
for c in set(copy_df.columns).difference(columns)):
copy_df.loc[:, c] = missing_remap[str(copy_df[c].dtype)]
new_df = copy_df.append(df, sort=False).reset_index(drop=True)
new_df.loc[0, columns] = 'Cindy', 42
我知道我一定错过了什么.
I know I must be missing something.
如你所见，由于 NaN 是 float，添加 NaN到一个系列可能会导致它被向上转换为 float 或转换为 object.您确定这不是一个理想的结果是正确的.
As you found, since NaN is a float, adding NaN to a series may cause it to be either upcasted to float or converted to object. You are right in determining this is not a desirable outcome.
没有直接的方法.我的建议是将您的输入行数据存储在字典中，并在附加之前将其与默认字典相结合.请注意，这是有效的，因为 pd.DataFrame.append 接受 dict 参数.
There is no straightforward approach. My suggestion is to store your input row data in a dictionary and combine it with a dictionary of defaults before appending. Note that this works because pd.DataFrame.append accepts a dict argument.
在 Python 3.6 中，您可以使用语法 {**d1, **d2} 组合两个字典，并优先选择第二个.
In Python 3.6, you can use the syntax {**d1, **d2} to combine two dictionaries with preference for the second.
default = {'name': '', 'age': 0, 'weight': 0.0, 'has_children': False}
row = {'name': 'Cindy', 'age': 42}
df = df.append({**default, **row}, ignore_index=True)
print(df)
age has_children name weight
0 45 True Bob 143.2
1 40 True Sue 130.2
2 10 False Tom 34.9
3 42 False Cindy 0.0
print(df.dtypes)
age int64
has_children bool
name object
weight float64
dtype: object
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