CODEWITHSUNDEEP
pythonpandas
Olaf Willocx
Olaf Willocx

Reputation: 65

Strange pandas nameless column

My csv file looks like this.

,timestamp,side,size,price,tickDirection,grossValue,homeNotional,foreignNotional
0,1569974396.557895,1,11668,8319.5,1,140248813.0,11668,1.40248813
1,1569974394.78865,0,5000,8319.0,0,60103377.0,5000,0.60103377
2,1569974392.355395,0,564,8319.0,0,6779660.999999999,564,0.06779661
3,1569974383.797042,0,100,8319.0,0,1202067.0,100,0.01202067
4,1569974382.944569,0,3,8319.0,0,36062.0,3,0.00036062
5,1569974382.944569,0,7412,8319.0,-1,89097247.0,7412,0.89097247

There's a nameless index column. I want to remove this column. When I read this in pandas, it just interprets it as an index and moves on. The problem is, when you now use df[::-1], it flips the indexes as well. So df[::-1]['timestamp][0] is the same as df['timestamp'][0] if the file was read with indexes, but not if it was read without.

How do I make it actually ignore the index column so that df[::-1] doesn't flip my indexes?

I tried usecols in read_csv, but it doesn't matter, it reads the indexes as well as the columns specified. I tried del df[''], but it doesn't work because it doesn't interpret the index column as column '', even though that's what it is.

Upvotes: 1

Views: 91

Answers (2)

Corralien
Corralien

Reputation: 120391

Just use index_col=0

df = pd.read_csv('data.csv', index_col=0)
print(df)

# Output
      timestamp  side   size   price  tickDirection   grossValue  homeNotional  foreignNotional
0  1.569974e+09     1  11668  8319.5              1  140248813.0         11668         1.402488
1  1.569974e+09     0   5000  8319.0              0   60103377.0          5000         0.601034
2  1.569974e+09     0    564  8319.0              0    6779661.0           564         0.067797
3  1.569974e+09     0    100  8319.0              0    1202067.0           100         0.012021
4  1.569974e+09     0      3  8319.0              0      36062.0             3         0.000361
5  1.569974e+09     0   7412  8319.0             -1   89097247.0          7412         0.890972

Upvotes: 1

Thrasy
Thrasy

Reputation: 606

If I understand correctly you issue, you can just set timestamp as your index:

df.set_index('timestamp', drop = True)

Upvotes: 0

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