This is a blog post about bla...

In [1]:

import pandas as pd

In [2]:

df = pd.read_csv('train.csv', nrows=100)

In [3]:

df.head()

Out[3]:

	Id	Feature_1	Feature_2	Feature_3	Feature_4	Feature_5	Feature_6	Feature_7	Feature_8	Feature_9	...	Ret_175	Ret_176	Ret_177	Ret_178	Ret_179	Ret_180	Ret_PlusOne	Ret_PlusTwo	Weight_Intraday	Weight_Daily
0	1	NaN	NaN	NaN	NaN	8	NaN	75751	0.2254	11	...	-0.002688	0.002246	-0.000838	-6.953224e-04	0.000003	-0.001974	-0.019512	0.028846	1251507.965681	1564384.957101
1	2	NaN	NaN	NaN	NaN	3	0.388896	17369	0.0166	13	...	-0.000129	0.000123	0.000248	3.315418e-07	0.000003	0.000027	-0.002939	-0.010253	1733950.349065	2167437.936331
2	3	NaN	-0.696727	0.739591	-0.167928	9	0.471947	8277	0.3650	9	...	-0.000524	-0.000394	0.000116	5.322557e-04	0.000274	0.000784	-0.024791	0.015711	1529197.383151	1911496.728938
3	4	NaN	-0.694350	1.568248	0.479073	5	0.120653	22508	0.2654	13	...	0.000346	-0.000090	0.000288	-1.281102e-04	0.000074	0.000341	-0.005680	-0.002190	1711569.423695	2139461.779618
4	5	6	-1.736489	2.765531	1.245280	7	4.866985	22423	0.2138	13	...	-0.001235	0.000027	0.002449	8.619882e-06	0.001209	-0.000004	0.036104	-0.026552	1267270.264754	1584087.830943

5 rows × 211 columns

In [5]:

%matplotlib inline
df.Feature_8.ix[:2000].plot.line()

Out[5]:

<matplotlib.axes._subplots.AxesSubplot at 0x10c5104e0>

Data Munging