Hello friends in this article we learn about how many iteration methods are used in pandas and we learn about only Class 12 CBSE important iteration methods like iterrows() and iteritems(). In this article we learn about with the help of the code in python. You will learn each and every iteration methods easily. Let us start this section.
Pandas provides several more convenient methods for iteration:
With .items() and .iteritems(), you iterate over the columns of a Pandas DataFrame. Each iteration yields a tuple with the name of the column and the column data as a Series object:
With .iterrows(), you iterate over the rows of a Pandas DataFrame. Each iteration yields a tuple with the name of the row and the row data as a Series object:
import pandas as pd
import numpy as np
data = {
'name': ['Xavier', 'Ann', 'Jana', 'Yi', 'Robin', 'Amal', 'Nori'],
'city': ['Mexico City', 'Toronto', 'Prague', 'Shanghai',
'Manchester', 'Cairo', 'Osaka'],
'age': [41, 28, 33, 34, 38, 31, 37],
'py-score': [88.0, 79.0, 81.0, 80.0, 68.0, 61.0, 84.0]}
row_labels = [101, 102, 103, 104, 105, 106, 107]
df = pd.DataFrame(data=data, index=row_labels)
print(df)
for i in df.iterrows():
print(i,"\n")
OUT PUT
name city age py-score
101 Xavier Mexico City 41 88.0
102 Ann Toronto 28 79.0
103 Jana Prague 33 81.0
104 Yi Shanghai 34 80.0
105 Robin Manchester 38 68.0
106 Amal Cairo 31 61.0
107 Nori Osaka 37 84.0
(101, name Xavier
city Mexico City
age 41
py-score 88
Name: 101, dtype: object)
(102, name Ann
city Toronto
age 28
py-score 79
Name: 102, dtype: object)
(103, name Jana
city Prague
age 33
py-score 81
Name: 103, dtype: object)
(104, name Yi
city Shanghai
age 34
py-score 80
Name: 104, dtype: object)
(105, name Robin
city Manchester
age 38
py-score 68
Name: 105, dtype: object)
(106, name Amal
city Cairo
age 31
py-score 61
Name: 106, dtype: object)
(107, name Nori
city Osaka
age 37
py-score 84
Name: 107, dtype: object)
--------------------------------------------------------------------------------------
import pandas as pd
import numpy as np
data = {
'name': ['Xavier', 'Ann', 'Jana', 'Yi', 'Robin', 'Amal', 'Nori'],
'city': ['Mexico City', 'Toronto', 'Prague', 'Shanghai',
'Manchester', 'Cairo', 'Osaka'],
'age': [41, 28, 33, 34, 38, 31, 37],
'py-score': [88.0, 79.0, 81.0, 80.0, 68.0, 61.0, 84.0]}
row_labels = [101, 102, 103, 104, 105, 106, 107]
df = pd.DataFrame(data=data, index=row_labels)
print(df)
for (i,j) in df.iterrows():
print(i,"\n")
#for row_data in j:
print(j,"\n")
# print("\n")
OutPut of the this code
name city age py-score
101 Xavier Mexico City 41 88.0
102 Ann Toronto 28 79.0
103 Jana Prague 33 81.0
104 Yi Shanghai 34 80.0
105 Robin Manchester 38 68.0
106 Amal Cairo 31 61.0
107 Nori Osaka 37 84.0
101
name Xavier
city Mexico City
age 41
py-score 88
Name: 101, dtype: object
102
name Ann
city Toronto
age 28
py-score 79
Name: 102, dtype: object
103
name Jana
city Prague
age 33
py-score 81
Name: 103, dtype: object
104
name Yi
city Shanghai
age 34
py-score 80
Name: 104, dtype: object
105
name Robin
city Manchester
age 38
py-score 68
Name: 105, dtype: object
106
name Amal
city Cairo
age 31
py-score 61
Name: 106, dtype: object
107
name Nori
city Osaka
age 37
py-score 84
Name: 107, dtype: object
---------------------------------------------------------------------------------------
import pandas as pd
import numpy as np
data = {
'name': ['Xavier', 'Ann', 'Jana', 'Yi', 'Robin', 'Amal', 'Nori'],
'city': ['Mexico City', 'Toronto', 'Prague', 'Shanghai',
'Manchester', 'Cairo', 'Osaka'],
'age': [41, 28, 33, 34, 38, 31, 37],
'py-score': [88.0, 79.0, 81.0, 80.0, 68.0, 61.0, 84.0]}
row_labels = [101, 102, 103, 104, 105, 106, 107]
df = pd.DataFrame(data=data, index=row_labels)
print(df)
for (i,j) in df.iterrows():
print(i,"\n")
for row_data in j:
print(row_data,"\n")
print("\n")
outPut of code
name city age py-score
101 Xavier Mexico City 41 88.0
102 Ann Toronto 28 79.0
103 Jana Prague 33 81.0
104 Yi Shanghai 34 80.0
105 Robin Manchester 38 68.0
106 Amal Cairo 31 61.0
107 Nori Osaka 37 84.0
101
Xavier
Mexico City
41
88.0
102
Ann
Toronto
28
79.0
103
Jana
Prague
33
81.0
104
Yi
Shanghai
34
80.0
105
Robin
Manchester
38
68.0
106
Amal
Cairo
31
61.0
107
Nori
Osaka
37
84.0
----------------------------------------------------------------------
import pandas as pd
import numpy as np
data = {
'name': ['Xavier', 'Ann', 'Jana', 'Yi', 'Robin', 'Amal', 'Nori'],
'city': ['Mexico City', 'Toronto', 'Prague', 'Shanghai',
'Manchester', 'Cairo', 'Osaka'],
'age': [41, 28, 33, 34, 38, 31, 37],
'py-score': [88.0, 79.0, 81.0, 80.0, 68.0, 61.0, 84.0]}
row_labels = [101, 102, 103, 104, 105, 106, 107]
df = pd.DataFrame(data=data, index=row_labels)
print(df)
for i in df.iteritems():
print(i,"\n")
#for row_data in j:
#print(j,"\n")
# print("\n")
name city age py-score
101 Xavier Mexico City 41 88.0
102 Ann Toronto 28 79.0
103 Jana Prague 33 81.0
104 Yi Shanghai 34 80.0
105 Robin Manchester 38 68.0
106 Amal Cairo 31 61.0
107 Nori Osaka 37 84.0
('name', 101 Xavier
102 Ann
103 Jana
104 Yi
105 Robin
106 Amal
107 Nori
Name: name, dtype: object)
('city', 101 Mexico City
102 Toronto
103 Prague
104 Shanghai
105 Manchester
106 Cairo
107 Osaka
Name: city, dtype: object)
('age', 101 41
102 28
103 33
104 34
105 38
106 31
107 37
Name: age, dtype: int64)
('py-score', 101 88.0
102 79.0
103 81.0
104 80.0
105 68.0
106 61.0
107 84.0
Name: py-score, dtype: float64)
---------------------------------------------------------------------------
import pandas as pd
import numpy as np
data = {
'name': ['Xavier', 'Ann', 'Jana', 'Yi', 'Robin', 'Amal', 'Nori'],
'city': ['Mexico City', 'Toronto', 'Prague', 'Shanghai',
'Manchester', 'Cairo', 'Osaka'],
'age': [41, 28, 33, 34, 38, 31, 37],
'py-score': [88.0, 79.0, 81.0, 80.0, 68.0, 61.0, 84.0]}
row_labels = [101, 102, 103, 104, 105, 106, 107]
df = pd.DataFrame(data=data, index=row_labels)
print(df)
for (i,j) in df.iteritems():
print(i,"\n")
#for row_data in j:
print(j,"\n")
# print("\n")
101 Xavier Mexico City 41 88.0
102 Ann Toronto 28 79.0
103 Jana Prague 33 81.0
104 Yi Shanghai 34 80.0
105 Robin Manchester 38 68.0
106 Amal Cairo 31 61.0
107 Nori Osaka 37 84.0
name
101 Xavier
102 Ann
103 Jana
104 Yi
105 Robin
106 Amal
107 Nori
Name: name, dtype: object
city
101 Mexico City
102 Toronto
103 Prague
104 Shanghai
105 Manchester
106 Cairo
107 Osaka
Name: city, dtype: object
age
101 41
102 28
103 33
104 34
105 38
106 31
107 37
Name: age, dtype: int64
py-score
101 88.0
102 79.0
103 81.0
104 80.0
105 68.0
106 61.0
107 84.0
Name: py-score, dtype: float64
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