Iterating over each element in pandas DataFrame

Question

So I got a pandas DataFrame with a single column and a lot of data.

I need to access each of the element, not to change it (with apply()) but to parse it into another function.

When looping through the DataFrame it always stops after the first one.

If I convert it to a list before, then my numbers are all in braces (eg. [12] instead of 12) thus breaking my code.

Does anyone see what I am doing wrong?

import pandas as pd

def go_trough_list(df):
  for number in df:
    print(number)

df = pd.read_csv("my_ids.csv")
go_trough_list(df)

df looks like:

   1
0  2
1  3
2  4
dtype: object
[Finished in 1.1s]

Edit: I found one mistake. My first value is recognized as a header. So I changed my code to:

df = pd.read_csv("my_ids.csv",header=None)

But with

for ix in df.index:
    print(df.loc[ix])

I get:

0    1
Name: 0, dtype: int64
0    2
Name: 1, dtype: int64
0    3
Name: 2, dtype: int64
0    4
Name: 3, dtype: int64

edit: Here is my Solution thanks to jezrael and Nick!

First I added headings=None because my data has no header. Then I changed my function to:

def go_through_list(df)
    new_list = df[0].apply(my_function,parameter=par1)
    return new_list

And it works perfectly! Thank you again guys, problem solved.

Answer 1

You can use the index as in other answers, and also iterate through the df and access the row like this:

for index, row in df.iterrows():
    print(row['column'])

however, I suggest solving the problem differently if performance is of any concern. Also, if there is only one column, it is more correct to use a Pandas Series.

What do you mean by parse it into another function? Perhaps take the value, and do something to it and create it into another column?

I need to access each of the element, not to change it (with apply()) but to parse it into another function.

Perhaps this example will help:

import pandas as pd
df = pd.DataFrame([20, 21, 12])
def square(x):
    return x**2
df['new_col'] = df[0].apply(square)  # can use a lambda here nicely

Answer 2

You can convert column as Series tolist:

for x in df['Colname'].tolist():
    print x

Sample:

import pandas as pd

df = pd.DataFrame({'a': pd.Series( [1, 2, 3]),
                   'b': pd.Series( [4, 5, 6])})
print df
   a  b
0  1  4
1  2  5
2  3  6

for x in df['a'].tolist():
    print x
    1
    2
    3

If you have only one column, use iloc for selecting first column:

for x in df.iloc[:,0].tolist():
    print x

Sample:

import pandas as pd

df = pd.DataFrame({1: pd.Series( [2, 3, 4])})
print df
   1
0  2
1  3
2  4

for x in df.iloc[:,0].tolist():
    print x
    2
    3
    4  

This can work too, but it is not recommended approach, because 1 can be number or string and it can raise Key error:

for x in df[1].tolist():
    print x
2
3
4