python - Pandas: Reindexing dataframe won't keep initial values -

i have dataframe consisting of 5 decreasing series (290 rows each) values comprised between 0 , 1.

the data looks that:

      b   c   d   e 0.60    0.998494    1.0 1.0 1.0 1.0 0.65    0.997792    1.0 1.0 1.0 1.0 0.70    0.996860    1.0 1.0 1.0 1.0 0.75    0.995359    1.0 1.0 1.0 1.0 0.80    0.992870    1.0 1.0 1.0 1.0 

i want reindex dataframe have 0.01 increments between each row. i've tried pd.dataframe.reindex no avail: returns dataframe of values np.nan

import pandas pd df = pd.read_csv('http://pastebin.com/raw/yehdk2gq', index_col=0) print df.reindex(np.arange(0.6, 3.5, 0.025)).head() 

which returns 2 valid rows, , converts 288 others nan:

      b   c   d   e 0.600   0.998494    1.0 1.0 1.0 1.0 0.625   nan nan nan nan nan 0.650   0.997792    1.0 1.0 1.0 1.0 0.675   nan nan nan nan nan 0.700   nan nan nan nan nan ##this row existed before reindexing 

pandas can't match new index intial values, although there doesn't seem rounding issues (the initial index has no more 2 decimals).

this seems somehow related data following works intended:

df = pd.dataframe(np.random.randn(10,3), columns=['a', 'b', 'c'])\        .reindex(np.arange(1, 10, 0.5)) print df.head() 

which gives:

                    b         c 1.0  0.206539  0.346656  2.578709 1.5       nan       nan       nan 2.0  1.164226  2.693394  1.183696 2.5       nan       nan       nan 3.0 -0.532072 -1.044149  0.818853 

thanks help!

this because precision of numpy.

in [31]: np.arange(0.6, 3.5, 0.025).tolist()[0:10]  out[31]:  [0.6, 0.625, 0.65, 0.675, 0.7000000000000001, 0.7250000000000001,   0.7500000000000001, 0.7750000000000001, 0.8000000000000002, 0.8250000000000002]