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'])
# Option 1
for row in df.iterrows():
print row.loc[0,'A']
print row.A
print row.index()
# Option 2
for i in range(len(df)) :
print(df.iloc[i, 0], df.iloc[i, 2])
import pandas as pd
import numpy as np
df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})
for index, row in df.iterrows():
print(row['c1'], row['c2'])
# Method A for single column dataframe
cell = list()
for i in range(len(df)):
cell_value=df.iloc[i][0]
cell.append(cell_value)
# Method B for multiple column dataframe
for index, row in df.iterrows():
print(row["c1"], row["c2"])
# Method C
columns = list(df.columns)
for i in columns:
print (df[i][2])
df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
# creating a list of dataframe columns
columns = list(df)
for i in columns:
# printing the third element of the column
print (df[i][2])