I have a DataFrame
from pandas:
import pandas as pd
inp = [{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}]
df = pd.DataFrame(inp)
print df
Output:
c1 c2
0 10 100
1 11 110
2 12 120
Now I want to iterate over the rows of this frame. For every row I want to be able to access its elements (values in cells) by the name of the columns. For example:
for row in df.rows:
print row['c1'], row['c2']
Is it possible to do that in pandas?
I found this similar question. But it does not give me the answer I need. For example, it is suggested there to use:
for date, row in df.T.iteritems():
or
for row in df.iterrows():
But I do not understand what the row
object is and how I can work with it.
Answer
DataFrame.iterrows is a generator which yield both index and row
import pandas as pd
import numpy as np
df = pd.DataFrame([{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}])
for index, row in df.iterrows():
print(row['c1'], row['c2'])
Output:
10 100
11 110
12 120
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