You also need to be careful to create a copy of the DataFrame, otherwise the csvdata_old will be updated with csvdata (since it points to the same object):

csvdata_old = csvdata.copy()

To check whether they are equal, you can use assert_frame_equal as in this answer:

from pandas.util.testing import assert_frame_equal
assert_frame_equal(csvdata, csvdata_old)

You can wrap this in a function with something like:

try:
    assert_frame_equal(csvdata, csvdata_old)
    return True
except:  # appeantly AssertionError doesn't catch all
    return False

There was discussion of a better way...

This again is a subtle one, well done for spotting it.

import pandas as pd
df_1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df_2 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
r = pd.DataFrame({'a': ['x'], 'b': ['y']})
df_1 = df_1.append(r, ignore_index=True)
df_1 = pd.concat([df_1, r]).drop_duplicates(keep=False)
df_1.equals(df_2)

from pandas.util.testing import assert_frame_equal
assert_frame_equal(df_1,df_2)

Now we can see the issue as the assert fails.

AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="a") are different

Attribute "dtype" are different
[left]:  object
[right]: int64

as you added strings to integers the integers became objects. so this is why the equals fails as well..