pandas read_csv 导入为列提供混合类型
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pandas read_csv import gives mixed type for a column
提问by lessthanl0l
I have a csv file that contains 130,000 rows. After reading in the file using pandas' read_csv function, one of the Column("CallGuid") has mixed object types.
我有一个包含 130,000 行的 csv 文件。使用 pandas 的 read_csv 函数读入文件后，其中一个 Column("CallGuid") 具有混合对象类型。
I did:
我做了：
df = pd.read_csv("data.csv")
Then I have this:
然后我有这个：
In [10]: df["CallGuid"][32767]
Out[10]: 4129237051L
In [11]: df["CallGuid"][32768]
Out[11]: u'4129259051'
All rows <= 32767 are of type longand all rows > 32767 are unicode
所有行 <= 32767 都是类型long，所有行 > 32767 都是unicode
Why is this?
为什么是这样？
回答by paulo.filip3
As others have pointed out, your data could be malformed, like having quotes or something...
正如其他人指出的那样，您的数据可能格式不正确，例如有引号或其他东西......
Just try doing:
试着做：
import pandas as pd
import numpy as np
df = pd.read_csv("data.csv", dtype={"CallGuid": np.int64})
It's also more memory efficient, since pandas doesn't have to guess the data types.
它还具有更高的内存效率，因为 Pandas 不必猜测数据类型。
回答by WNG
OK I just experienced the same problem, with the same symptom : df[column][n] changed type after n>32767
好的，我刚刚遇到了同样的问题，具有相同的症状：df[column][n] 在 n>32767 后更改了类型
I indeed had a problem in my data, but not at all at line 32767
我的数据确实有问题，但在第 32767 行根本没有问题
Finding and modifying these few problematic lines solved my problem. I managed to localize the line that was problematic by using the following extremely dirty routine :
查找和修改这几行有问题的行解决了我的问题。我设法通过使用以下极其肮脏的例程来定位有问题的行：
df = pd.read_csv('data.csv',chunksize = 10000)
i=0
for chunk in df:
print "{} {}".format(i,chunk["Custom Dimension 02"].dtype)
i+=1
I ran this and I obtained :
我跑了这个，我得到了：
0 int64
1 int64
2 int64
3 int64
4 int64
5 int64
6 object
7 int64
8 object
9 int64
10 int64
Which told me that there was (at least) one problematic line between 60000 and 69999 and one between 80000 and 89999
这告诉我在 60000 和 69999 之间有（至少）一条有问题的线，在 80000 和 89999 之间有一条线
To localize them more precisely, you can just take a smaller chunksize and print only the number of the rows that do not have the correct dta type
为了更精确地定位它们，您可以采用较小的块大小并仅打印没有正确 dta 类型的行数
