Condition in Pandas - pandas

Question

I have a very peculiar problem in Pandas: one condition works but the other does not. You may download the linked file to test my code. Thanks!
I have a file (stars.txt) that I read in with Pandas. I would like to create two groups: (1) with Log_g < 4.0 and (2) Log_g > 4.0. In my code (see below) I can successfully get rows for group (1):
Kepler_ID RA Dec Teff Log_G g H
3 2305372 19 27 57.679 +37 40 21.90 5664 3.974 14.341 12.201
14 2708156 19 21 08.906 +37 56 11.44 11061 3.717 10.672 10.525
19 2997455 19 32 31.296 +38 07 40.04 4795 3.167 14.694 11.500
34 3352751 19 36 17.249 +38 25 36.91 7909 3.791 13.541 12.304
36 3440230 19 21 53.100 +38 31 42.82 7869 3.657 13.706 12.486
But for some reason I cannot get (2). The code returns the following for error:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 90 entries, 0 to 108
Data columns (total 7 columns):
Kepler_ID 90 non-null values
RA 90 non-null values
Dec 90 non-null values
Teff 90 non-null values
Log_G 90 non-null values
g 90 non-null values
H 90 non-null values
dtypes: float64(4), int64(1), object(2)
Here's my code:
#------------------------------------------
# IMPORT STATEMENTS
#------------------------------------------
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#------------------------------------------
# READ FILE AND ASSOCIATE COMPONENTS
#------------------------------------------
star_file = 'stars.txt'
header_row = ['Kepler_ID', 'RA','Dec','Teff', 'Log_G', 'g', 'H']
df = pd.read_csv(star_file, names=header_row, skiprows=2)
#------------------------------------------
# ASSOCIATE VARIABLES
#------------------------------------------
Kepler_ID = df['Kepler_ID']
#RA = df['RA']
#Dec = df['Dec']
Teff = df['Teff']
Log_G = df['Log_G']
g = df['g']
H = df['H']
#------------------------------------------
# SUBSTITUTE MISSING DATA WITH NAN
#------------------------------------------
df = df.replace('', np.nan)
#------------------------------------------
# CHANGE DATA TYPE OF THE REST OF DATA TO FLOAT
#------------------------------------------
df[['Teff', 'Log_G', 'g', 'H']] = df[['Teff', 'Log_G', 'g', 'H']].astype(float)
#------------------------------------------
# SORTING SPECTRA TYPES FOR GIANTS
#------------------------------------------
# FIND GIANTS IN THE SAMPLE
giants = df[(df['Log_G'] < 4.)]
#print giants
# FIND GIANTS IN THE SAMPLE
dwarfs = df[(df['Log_G'] > 4.)]
print dwarfs

Answer 1

This is not an error. You are seeing a summarized view of the DataFrame:
In [11]: df = pd.DataFrame([[2, 1], [3, 4]])
In [12]: df
Out[12]:
0 1
0 2 1
1 3 4
In [13]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
0 2 non-null values
1 2 non-null values
dtypes: int64(2)
Which is displayed is decided by several display package options, for example, max_rows:
In [14]: pd.options.display.max_rows
Out[14]: 60
In [15]: pd.options.display.max_rows = 120
In 0.13, this behaviour changed, so you will see the first max_rows followed by ....