You can do it that way:

# for Python 2
df.index = df.index.map(unicode) 

# for Python 3 (the unicode type does not exist and is replaced by str)
df.index = df.index.map(str)

As for why you would proceed differently from when you'd convert from int to float, that's a peculiarity of numpy (the library on which pandas is based).

Every numpy array has a dtype, which is basically the machine type of its elements : in that manner, numpy deals directly with native types, not with Python objects, which explains how it is so fast. So when you are changing the dtype from int64 to float64, numpy will cast each element in the C code.

There's also a special dtype : object, that will basically provide a pointer toward a Python object.

If you want strings, you thus have to use the object dtype. But using .astype(object) would not give you the answer you were looking for : it would instead create an index with object dtype, but put Python float objects inside.

Here, by using map, we convert the index to strings with the appropriate function: numpy gets the string objects and understand that the index has to have an object dtype, because that's the only dtype that can accomodate strings.

Despite my bloviating in the comments above, this might suffice to get an appropriate index that is both low memory and starts from -1.

pandas.RangeIndex

Takes a start and stop parameters like range

df = df.set_index(pd.RangeIndex(-1, len(df) - 1))

print(df.index, df.index.dtype, sep='\n')

This should be very memory efficient.

Despite it still being of dtype int64 (which you should want), it takes up very little memory.

pd.RangeIndex(-1, 4000000).memory_usage()

84

And

for i in range(1, 1000000, 100000):
  print(pd.RangeIndex(-1, i).memory_usage())

84
84
84
84
84
84
84
84
84
84