使用其位置/索引转换列的类型

Question

I am reading some .csv files from a folder. I am trying to create a list of data frames from using each file.

In some files the column values, ie Quantity is in str and float64 data types. Therefore, I am trying to convert the that column quantity into int .

I am accessing my columns using its position/index (For automation purposes).

Out of all data frames from a list, this is one of them,

    CustName    ProductID   Quantity
0   56MED       110         '1215.0'
1   56MED       112         5003.0
2   56MED       114         '6822.0'
3   WillSup     2285        5645.0
4   WillSup     5622        6523.0
5   HammSup     9522        1254.0
6   HammSup     6954        5642.0

Therefore, I have my looks like this,

df.columns[2] = pd.to_numeric(df.columns[2], errors='coerce').astype(str).astype(np.int64)

I am getting,

TypeError: Index does not support mutable operations

Prior to this, I tried,

df.columns[2] = pd.to_numeric(df.columns[2], errors='coerce').fillna(0).astype(str).astype(np.int64)

However, I got this error,

AttributeError: 'numpy.float64' object has no attribute 'fillna'

There are posts that have using column names directly, but not columns position. How can I convert my column into int using the column position/index in pnadas ?

My pandas version

print(pd.__version__)
>> 0.23.3

Answer 1

df.columns[2] returns a scalar , in this case a string.

To access a series use either df['Quantity'] or df.iloc[:, 2] , or even df[df.columns[2]] . Instead of the repeated transformations, if you are sure you have data which should be integers, use downcast='integer' .

All these are equivalent:

df['Quantity'] = pd.to_numeric(df['Quantity'], errors='coerce', downcast='integer')

df.iloc[:, 2] = pd.to_numeric(df.iloc[:, 2], errors='coerce', downcast='integer')

df[df.columns[2]] = pd.to_numeric(df[df.columns[2]], errors='coerce', downcast='integer')

Answer 2

Try this, you need to remove those quotes from your strings first, then use pd.to_numeric :

df.iloc[:, 2] = pd.to_numeric(df.iloc[:, 2].str.strip('\'')).astype(int)

OR from @jpp:

df['Quantity'] = pd.to_numeric(df['Quantity'].str.strip('\''), errors='coerce', downcast='integer')

Output, df.info():

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7 entries, 0 to 6
Data columns (total 3 columns):
CustName     7 non-null object
ProductID    7 non-null int64
Quantity     7 non-null int32
dtypes: int32(1), int64(1), object(1)
memory usage: 196.0+ bytes

Output:

  CustName  ProductID  Quantity
0    56MED        110      1215
1    56MED        112      5003
2    56MED        114      6822
3  WillSup       2285      5645
4  WillSup       5622      6523
5  HammSup       9522      1254
6  HammSup       6954      5642