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Python Pandas - Iteration
Neha Kumawat
a year ago
Table of Content
- Iterating a DataFrame
- iteritems()
- iterrows()
- itertuples()
The behavior of basic iteration over Pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Other data structures, like DataFrame and Panel, follow the dict-like convention of iterating over the keys of the objects.
In short, basic iteration (for i in object) produces −
- Series − values
- DataFrame − column labels
- Panel − item labels
Iterating a DataFrame
Iterating a DataFrame gives column names. Let us consider the following example to understand the same.
import pandas as pd
import numpy as np
N=20
df = pd.DataFrame({
'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
'x': np.linspace(0,stop=N-1,num=N),
'y': np.random.rand(N),
'C': np.random.choice(['Low','Medium','High'],N).tolist(),
'D': np.random.normal(100, 10, size=(N)).tolist()
})
for col in df:
print(col)
Its output is as follows −
A
C
D
x
y
To iterate over the rows of the DataFrame, we can use the following functions −
- iteritems() − to iterate over the (key,value) pairs
- iterrows() − iterate over the rows as (index, series) pairs
- itertuples() − iterate over the rows as named tuples
iteritems()
Iterates over each column as key, value pair with the label as key, and column value as a Series object.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(4,3),columns=['col1','col2','col3'])
for key,value in df.iteritems():
print (key,value)
Its output is as follows −
col1 0 0.802390
1 0.324060
2 0.256811
3 0.839186
Name: col1, dtype: float64
col2 0 1.624313
1 -1.033582
2 1.796663
3 1.856277
Name: col2, dtype: float64
col3 0 -0.022142
1 -0.230820
2 1.160691
3 -0.830279
Name: col3, dtype: float64
Observe, each column is iterated separately as a key-value pair in a Series.
iterrows()
iterrows() returns the iterator yielding each index value along with a series containing the data in each row.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
for row_index,row in df.iterrows():
print(row_index,row)
Its output is as follows −
0 col1 1.529759
col2 0.762811
col3 -0.634691
Name: 0, dtype: float64
1 col1 -0.944087
col2 1.420919
col3 -0.507895
Name: 1, dtype: float64
2 col1 -0.077287
col2 -0.858556
col3 -0.663385
Name: 2, dtype: float64
3 col1 -1.638578
col2 0.059866
col3 0.493482
Name: 3, dtype: float64
Note − Because iterrows() iterate over the rows, it doesn't preserve the data type across the row. 0,1,2 are the row indices and col1,col2,col3 are column indices.
itertuples()
itertuples() method will return an iterator yielding a named tuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
for row in df.itertuples():
print(row)
Its output is as follows −
Pandas(Index=0, col1=1.5297586201375899, col2=0.76281127433814944, col3=-
0.6346908238310438)
Pandas(Index=1, col1=-0.94408735763808649, col2=1.4209186418359423, col3=-
0.50789517967096232)
Pandas(Index=2, col1=-0.07728664756791935, col2=-0.85855574139699076, col3=-
0.6633852507207626)
Pandas(Index=3, col1=0.65734942534106289, col2=-0.95057710432604969,
col3=0.80344487462316527)
Note − Do not try to modify any object while iterating. Iterating is meant for reading and the iterator returns a copy of the original object (a view), thus the changes will not reflect on the original object.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
for index, row in df.iterrows():
row['a'] = 10
print(df)
Its output is as follows −
col1 col2 col3
0 -1.739815 0.735595 -0.295589
1 0.635485 0.106803 1.527922
2 -0.939064 0.547095 0.038585
3 -1.016509 -0.116580 -0.523158
Observe, no changes reflected.
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