Adding multiple rows in an existing dataframe

Question

Hi I'm learning data science and am trying to make a big data company list from a list with companies in various industries.

I have a list of row numbers for big data companies, named comp_rows. Now, I'm trying to make a new dataframe with the filtered companies based on the row numbers. Here I need to add rows to an existing dataframe but I got an error. Could someone help?

my datarame looks like this.

    company_url company tag_line    product data
0   https://angel.co/billguard  BillGuard   The fastest smartest way to track your spendin...   BillGuard is a personal finance security app t...   New York City · Financial Services · Security ...
1   https://angel.co/tradesparq Tradesparq  The world's largest social network for global ...   Tradesparq is Alibaba.com meets LinkedIn. Trad...   Shanghai · B2B · Marketplaces · Big Data · Soc...
2   https://angel.co/sidewalk   Sidewalk    Hoovers (D&B) for the social era    Sidewalk helps companies close more sales to s...   New York City · Lead Generation · Big Data · S...
3   https://angel.co/pangia Pangia  The Internet of Things Platform: Big data mana...   We collect and manage data from sensors embedd...   San Francisco · SaaS · Clean Technology · Big ...
4   https://angel.co/thinknum   Thinknum    Financial Data Analysis Thinknum is a powerful web platform to value c...   New York City · Enterprise Software · Financia...

My code is below:

bigdata_comp = DataFrame(data=None,columns=['company_url','company','tag_line','product','data'])

for count, item in enumerate(data.iterrows()):
    for number in comp_rows:
        if int(count) == int(number):
            bigdata_comp.append(item)

Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-234-1e4ea9bd9faa> in <module>()
      4     for number in comp_rows:
      5         if int(count) == int(number):
----> 6             bigdata_comp.append(item)
      7 

/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/frame.pyc in append(self, other, ignore_index, verify_integrity)
   3814         from pandas.tools.merge import concat
   3815         if isinstance(other, (list, tuple)):
-> 3816             to_concat = [self] + other
   3817         else:
   3818             to_concat = [self, other]

TypeError: can only concatenate list (not "tuple") to list

There's probably a way to do this without loops using indexing or boolean indexing. Please post your desired output for clarification — Bob Haffner
– Bob Haffner, Commented May 6, 2015 at 17:39

fixxxer · Accepted Answer · 2015-05-06 20:08:07Z

It seems you are trying to filter out an existing dataframe based on indices (which are stored in your variable called comp_rows). You can do this without using loops by using loc, like shown below:

In [1161]: df1.head()
Out[1161]: 
          A         B         C         D
a  1.935094 -0.160579 -0.173458  0.433267
b  1.669632 -1.130893 -1.210353  0.822138
c  0.494622  1.014013  0.215655  1.045139
d -0.628889  0.223170 -0.616019 -0.264982
e -0.823133  0.385790 -0.654533  0.582255

We will get the rows with indices 'a','b' and 'c', for all columns:

In [1162]: df1.loc[['a','b','c'],:]
Out[1162]: 
          A         B         C         D
a  1.935094 -0.160579 -0.173458  0.433267
b  1.669632 -1.130893 -1.210353  0.822138
c  0.494622  1.014013  0.215655  1.045139

You can read more about it here.

About your code:

1. You do not need to iterate through a list to see if an item is present in it: Use the in operator. For example -

In [1199]: 1 in [1,2,3,4,5]
Out[1199]: True

so, instead of

for number in comp_rows:
        if int(count) == int(number):

do this

if number in comp_rows

2. pandas append does not happen in-place. You have to store the result into another variable. See here.

3.

Append one row at a time is a slow way to do what you want. Instead, save each row that you want to add into a list of lists, make a dataframe of it and append it to the target dataframe in one-go. Something like this..

temp = []
for count, item in enumerate(df1.loc[['a','b','c'],:].iterrows()):
    # if count in comp_rows:
    temp.append( list(item[1]))

## -- End pasted text --

In [1233]: temp
Out[1233]: 
[[1.9350940285526077,
  -0.16057932637141861,
  -0.17345827000000605,
  0.43326722021644282],
 [1.66963201034217,
  -1.1308932586268696,
  -1.2103527446031515,
  0.82213753819050794],
 [0.49462218161377397,
  1.0140133740187862,
  0.2156547595968879,
  1.0451391564351897]]

In [1236]: df2 = df1.append(pd.DataFrame(temp, columns=['A','B','C','D']))

In [1237]: df2
Out[1237]: 
          A         B         C         D
a  1.935094 -0.160579 -0.173458  0.433267
b  1.669632 -1.130893 -1.210353  0.822138
c  0.494622  1.014013  0.215655  1.045139
d -0.628889  0.223170 -0.616019 -0.264982
e -0.823133  0.385790 -0.654533  0.582255
f -0.872135  2.938475 -0.099367 -1.472519
0  1.935094 -0.160579 -0.173458  0.433267
1  1.669632 -1.130893 -1.210353  0.822138
2  0.494622  1.014013  0.215655  1.045139

Kathirmani Sukumar · Accepted Answer · 2015-05-06 16:32:28Z

0

Replace the following line:

for count, item in enumerate(data.iterrows()):

by

for count, (index, item) in enumerate(data.iterrows()):

or even simply as

for count, item in data.iterrows():

answered May 6, 2015 at 16:32

Kathirmani Sukumar

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