Access 1st column in Pandas dataframe - python-3.x

Question

How do I access the first column in this dataframe?
If I refer to it by the column name ('Group11...'), I get an error 'Not in index'.
First Column

Answer 1

iloc return data based on a numeric index, here all rows for the first (python 0-indexed) column.
df.iloc[:,0]

Answer 2

What you are refering to is the index of the dataframe. So, if your dataframe is called df, you can access the index using df.index.
Otherwise, if you want to refer to the as a column, you need to turn it into a column before using pandas.DataFrame.reset_index.
reproducible example:
Here's a reproducible example showing the two methods of accessing the index:
from StringIO import StringIO
import pandas as pd
data = """Group11.Primary.Phrase|count|num_cat
CP|4|4
DA|1|1
FW|7|7
"""
df = pd.read_csv(StringIO(data), sep="|", index_col=0)
print("here's how the dataframe looks like")
print(df.head())
print("here's how to access the index")
print(df.index)
print("if you want to turn the index values into a list")
print(list(df.index))
print("you can also reset_index as a column and access it")
df = df.reset_index()
print(df["Group11.Primary.Phrase"])
Running the above code, gives you the following output:
here's how the dataframe looks like
count num_cat
Group11.Primary.Phrase
CP 4 4
DA 1 1
FW 7 7
here's how to access the index
Index([u'CP', u'DA', u'FW'], dtype='object', name=u'Group11.Primary.Phrase')
if you want to turn the index values into a list
['CP', 'DA', 'FW']
you can also reset_index as a column and access it
0 CP
1 DA
2 FW
Name: Group11.Primary.Phrase, dtype: object

Answer 3

You can reset the index and then access column by the column name if you want to access it using column name. i.e
If you have a dataframe like
count num_cat
Group11.Primary.Phrase
CP 4 4
DA 1 1
FW 7 7
Then after resetting index when you access the column by its name then
df = df.reset_index()
df['Group11.Primary.Phrase']
Output:
0 CP
1 DA
2 FW

Answer 4

Here is a link to the docs: Indexing and Selecting Data In your case, you would index df['Group11']
In [9]: df
Out[9]:
A B C D
2000-01-01 0.469112 -0.282863 -1.509059 -1.135632
2000-01-02 1.212112 -0.173215 0.119209 -1.044236
2000-01-03 -0.861849 -2.104569 -0.494929 1.071804
In [12]: df[['A', 'B']]
Out[12]:
A B
2000-01-01 -0.282863 0.469112
2000-01-02 -0.173215 1.212112
2000-01-03 -2.104569 -0.861849