How To Print A Groupby Object

I want to print the result of grouping with Pandas. I have a dataframe: import pandas as pd df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})

Solution 1:

Simply do:

grouped_df = df.groupby('A')

for key, item in grouped_df:
    print(grouped_df.get_group(key), "\n\n")

---

Deprecation Notice: ix was deprecated in 0.20.0

This also works,

grouped_df = df.groupby('A')    
gb = grouped_df.groups

for key, values in gb.iteritems():
    print(df.ix[values], "\n\n")

For selective key grouping: Insert the keys you want inside the key_list_from_gb, in following, using gb.keys(): For Example,

gb = grouped_df.groups
gb.keys()

key_list_from_gb = [key1, key2, key3]

for key, values in gb.items():
    if key in key_list_from_gb:
        print(df.ix[values], "\n")

Solution 2:

If you're simply looking for a way to display it, you could use describe():

grp = df.groupby['colName']
grp.describe()

This gives you a neat table.

Solution 3:

In addition to previous answers:

Taking your example,

df = pd.DataFrame({'A': ['one', 'one', 'two', 'three', 'three', 'one'], 'B': range(6)})

Then simple 1 line code

df.groupby('A').apply(print)

Solution 4:

In Jupyter Notebook, if you do the following, it prints a nice grouped version of the object. The apply method helps in creation of a multiindex dataframe.

by = 'A'# groupby 'by' argument
df.groupby(by).apply(lambda a: a[:])

Output:

             A  B
A                
one0one01one15one5
three 3  three  34  three  4
two   2    two  2

If you want the by column(s) to not appear in the output, just drop the column(s), like so.

df.groupby(by).apply(lambda a: a.drop(by, axis=1)[:])

Output:

BA         
one   001155
three 3344
two   22

Here, I am not sure as to why .iloc[:] does not work instead of [:] at the end. So, if there are some issues in future due to updates (or at present), .iloc[:len(a)] also works.

Solution 5:

I confirmed that the behavior of head() changes between version 0.12 and 0.13. That looks like a bug to me. I created an issue.

But a groupby operation doesn't actually return a DataFrame sorted by group. The .head() method is a little misleading here -- it's just a convenience feature to let you re-examine the object (in this case, df) that you grouped. The result of groupby is separate kind of object, a GroupBy object. You must apply, transform, or filter to get back to a DataFrame or Series.

If all you wanted to do was sort by the values in columns A, you should use df.sort('A').