Pandas reading csv as string type

I have a data frame with alpha-numeric keys which I want to save as a csv and read back later. For various reasons I need to explicitly read this key column as a string format, I have keys which are strictly numeric or even worse, things like: 1234E5 which Pandas interprets as a float. This obviously makes the key completely useless.

The problem is when I specify a string dtype for the data frame or any column of it I just get garbage back. I have some example code here:

df = pd.DataFrame(np.random.rand(2,2),
                  index=['1A', '1B'],
                  columns=['A', 'B'])
df.to_csv(savefile)

The data frame looks like:

           A         B
1A  0.209059  0.275554
1B  0.742666  0.721165

Then I read it like so:

df_read = pd.read_csv(savefile, dtype=str, index_col=0)

and the result is:

   A  B
B  (  <
       

Is this a problem with my computer, or something I'm doing wrong here, or just a bug?

Update: this has been fixed: from 0.11.1 you passing str/np.str will be equivalent to using object.

Use the object dtype:

In [11]: pd.read_csv('a', dtype=object, index_col=0)
Out[11]:
                      A                     B
1A  0.35633069074776547     0.745585398803751
1B  0.20037376323337375  0.013921830784260236

or better yet, just don't specify a dtype:

In [12]: pd.read_csv('a', index_col=0)
Out[12]:
           A         B
1A  0.356331  0.745585
1B  0.200374  0.013922

but bypassing the type sniffer and truly returning only strings requires a hacky use of converters:

In [13]: pd.read_csv('a', converters={i: str for i in range(100)})
Out[13]:
                      A                     B
1A  0.35633069074776547     0.745585398803751
1B  0.20037376323337375  0.013921830784260236

where 100 is some number equal or greater than your total number of columns.

It's best to avoid the str dtype, see for example here.

Like Anton T said in his comment, pandas will randomly turn object types into float types using its type sniffer, even you pass dtype=object, dtype=str, or dtype=np.str.

Since you can pass a dictionary of functions where the key is a column index and the value is a converter function, you can do something like this (e.g. for 100 columns).

pd.read_csv('some_file.csv', converters={i: str for i in range(0, 100)})

You can even pass range(0, N) for N much larger than the number of columns if you don't know how many columns you will read.

Nowadays, (pandas==1.0.5) it just works.

pd.read_csv(f, dtype=str) will read everything as string except for NAN values (empty string, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’ if you don't want this strings to be parse as NAN use na_filter=False)

Use a converter that applies to any column if you don't know the columns before hand:

import pandas as pd

class StringConverter(dict):
    def __contains__(self, item):
        return True

    def __getitem__(self, item):
        return str

    def get(self, default=None):
        return str

pd.read_csv(file_or_buffer, converters=StringConverter())

Many of the above answers are fine but neither very elegant nor universal. If you want to read all of the columns as strings you can use the following construct without caring about the number of the columns.

from collections import defaultdict
import pandas as pd

pd.read_csv(file_or_buffer, converters=defaultdict(lambda i: str))

The defaultdict will return str for every index passed into converters.