In [16]: df = DataFrame(np.arange(10).reshape(5,2),columns=list('AB'))

In [17]: df
Out[17]: 
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9

In [18]: df.dtypes
Out[18]: 
A    int64
B    int64
dtype: object

Convert a series

In [19]: df['A'].apply(str)
Out[19]: 
0    0
1    2
2    4
3    6
4    8
Name: A, dtype: object

In [20]: df['A'].apply(str)[0]
Out[20]: '0'

Don't forget to assign the result back:

df['A'] = df['A'].apply(str)

Convert the whole frame

In [21]: df.applymap(str)
Out[21]: 
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9

In [22]: df.applymap(str).iloc[0,0]
Out[22]: '0'

df = df.applymap(str)

The type object is actually string in pandas dataframe.

If you would like to retain the data as string, use df.to_excel() instead of df.to_csv. This is because when opening the CSV file, Excel will automatically convert the number data to numbers.

df1 = pd.DataFrame({'GL': [2311000200.0, 2312000600.0, 2330800100.0]})
df1.GL = df1.GL.astype('int64').astype('string')
df1.to_excel('test.xlsx', index=False)

You can try by doing df["Bare Nuclei"].astype(np.int64) but as far as I can see the problem is something else. Pandas first reads all the data to best estimate the data type for each column, then only makes the data frame. So, there must be some entries in the data frame which are not integer types, i.e., they may contain some letters. In that case, also typecasting should give an error. So you need to remove those entries before successfully making the table integer.

One way to convert to string is to use astype:

total_rows['ColumnID'] = total_rows['ColumnID'].astype(str)

However, perhaps you are looking for the to_json function, which will convert keys to valid json (and therefore your keys to strings):

In [11]: df = pd.DataFrame([['A', 2], ['A', 4], ['B', 6]])

In [12]: df.to_json()
Out[12]: '{"0":{"0":"A","1":"A","2":"B"},"1":{"0":2,"1":4,"2":6}}'

In [13]: df[0].to_json()
Out[13]: '{"0":"A","1":"A","2":"B"}'

Note: you can pass in a buffer/file to save this to, along with some other options...

I solved my problem as follows.

Op['dt'] = pd.to_datetime(Op['dt'], format='%Y%m%d')
Op['dt'] = pd.to_datetime(Op['dt'], unit = 's')
Op['dt'] = Op['dt'].fillna(datetime(2200,12,31))
Op['dt'] = Op['dt'].apply(lambda x: x.strftime('%d/%m/%Y'))

numpy.ndarray.tolist will do it:

a.tolist()

If your data is a pandas series you can call their tolist wrapper with the same result.