pandas select column by index
# A B C
# 0 1 3 5
# 1 2 4 6
column_B = a_dataframe.iloc[:, 1]
print(column_B)
# OUTPUT
# 0 3
# 1 4pandas select column by index
# A B C
# 0 1 3 5
# 1 2 4 6
column_B = a_dataframe.iloc[:, 1]
print(column_B)
# OUTPUT
# 0 3
# 1 4isolate row based on index pandas
dfObj.iloc[: , [0, 2]]select rows from dataframe pandas
from pandas import DataFrame
boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'],
'Price': [10,15,5,5,10,15,15,5]
}
df = DataFrame(boxes, columns= ['Color','Shape','Price'])
select_color = df.loc[df['Color'] == 'Green']
print (select_color)pandas column by index
DataFrame.iloc[:, n]get column index pandas
df = pd.read_csv('thanksgiving_w_age_income.csv')
# Obtain Column Indices:
gravy = df.columns.get_loc("Do you typically have gravy?")
meet_friends = df.columns.get_loc('Have you ever tried to meet up with hometown friends on Thanksgiving night?')
friendsgiving = df.columns.get_loc('Have you ever attended a "Friendsgiving?"')pandas df by row index
indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]Copyright © 2021 Codeinu
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