pandas 我怎么知道熊猫数据框单元格的类型
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How can I know the type of a pandas dataframe cell
提问by John Smith
I have a dataframe, for example:
我有一个数据框，例如：
1
1.3
2,5
4
5
With the following code, I am trying to know what are the types of the different cells of my pandas dataframe:
使用以下代码，我想知道我的 Pandas 数据帧的不同单元格的类型是什么：
for i in range (len(data.columns)) :
print (" lenth of columns : " + str(len(data.columns)) )
for j in range (len(data[i])) :
data[i][j]=re.sub(r'(\d*)\.(\d*)',r',',str(data[i][j]))
print(str(data[i][j]))
print(" est de type : "type(data[i][j]))
if str(data[i][j]).isdigit():
print(str(data[i][j]) + " contain a number " )
The problem is when a cell of the dataframe contain a dot, pandas thinks it is a string. So I used regex, in order to change the dot into a comma.
问题是当数据帧的一个单元格包含一个点时，pandas 认为它是一个字符串。所以我使用了正则表达式，以便将点更改为逗号。
But after that, the types of all my dataframe cells changed to string. My question is: How can I know if a cell of the dataframe is an int or a float? I already tried isinstance(x, int)
但在那之后，我所有数据帧单元格的类型都更改为字符串。我的问题是：我怎么知道数据帧的单元格是 int 还是 float？我已经试过了isinstance(x, int)
edit : How can I count the number of int and float, with the output of the df.apply(type) for example , I want to know how many cells of my column are int or float
编辑：如何计算 int 和 float 的数量，例如 df.apply(type) 的输出，我想知道我的列中有多少个单元格是 int 或 float
My second question is, why when I have 2.5 , the dataframe give him the str type ?
我的第二个问题是，为什么当我有 2.5 时，数据框会给他 str 类型？
0 <class 'int'>
1 <class 'str'>
2 <class 'float'>
3 <class 'float'>
4 <class 'int'>
5 <class 'str'>
6 <class 'str'>
Thanks.
谢谢。
回答by rafaelc
If you have a column with different types, e.g.
如果您有不同类型的列，例如
>>> df = pd.DataFrame(data = {"l": [1,"a", 10.43, [1,3,4]]})
>>> df
l
0 1
1 a
2 10.43
4 [1, 3, 4]
Pandas will just state that this Seriesis of dtype object. However, you can get each entry type by simply applying typefunction
Pandas 只会声明这Series是 dtype object。但是，您可以通过简单地应用type函数来获取每个条目类型
>>> df.l.apply(type)
0 <type 'int'>
1 <type 'str'>
2 <type 'float'>
4 <type 'list'>
However, if you have a dataset with very different data types, you probably should reconsider its design..
但是，如果您有一个数据类型非常不同的数据集，您可能应该重新考虑其设计。
