Selecting pandas data using “iloc”
The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.
# Single selections using iloc and DataFrame # Rows: data.iloc[0] # first row of data frame (Aleshia Tomkiewicz) - Note a Series data type output. data.iloc[1] # second row of data frame (Evan Zigomalas) data.iloc[-1] # last row of data frame (Mi Richan) # Columns: data.iloc[:,0] # first column of data frame (first_name) data.iloc[:,1] # second column of data frame (last_name) data.iloc[:,-1] # last column of data frame (id)
# Multiple row and column selections using iloc and DataFrame data.iloc[0:5] # first five rows of dataframe data.iloc[:, 0:2] # first two columns of data frame with all rows data.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns. data.iloc[0:5, 5:8] # first 5 rows and 5th, 6th, 7th columns of data frame (county -> phone1).
Selecting pandas data using “loc”
The Pandas loc indexer can be used with DataFrames for two different use cases:
a.) Selecting rows by label/index
b.) Selecting rows with a boolean / conditional lookup
# Select rows with index values 'Andrade' and 'Veness', with all columns between 'city' and 'email'
data.loc[['Andrade', 'Veness'], 'city':'email']
# Select same rows, with just 'first_name', 'address' and 'city' columns
data.loc['Andrade':'Veness', ['first_name', 'address', 'city']]
# Change the index to be based on the 'id' column
data.set_index('id', inplace=True)
# select the row with 'id' = 487
data.loc[487]
# Select rows with first name Antonio, # and all columns between 'city' and 'email'
data.loc[data['first_name'] == 'Antonio', 'city':'email']
# Select rows where the email column ends with 'hotmail.com', include all columns
data.loc[data['email'].str.endswith("hotmail.com")]
# Select rows with last_name equal to some values, all columns
data.loc[data['first_name'].isin(['France', 'Tyisha', 'Eric'])]
# Select rows with first name Antonio AND hotmail email addresses
data.loc[data['email'].str.endswith("gmail.com") & (data['first_name'] == 'Antonio')]
# select rows with id column between 100 and 200, and just return 'postal' and 'web' columns
data.loc[(data['id'] > 100) & (data['id'] <= 200), ['postal', 'web']]
# A lambda function that yields True/False values can also be used.
# Select rows where the company name has 4 words in it.
data.loc[data['company_name'].apply(lambda x: len(x.split(' ')) == 4)]
# Selections can be achieved outside of the main .loc for clarity:
# Form a separate variable with your selections:
idx = data['company_name'].apply(lambda x: len(x.split(' ')) == 4)
# Select only the True values in 'idx' and only the 3 columns specified:
data.loc[idx, ['email', 'first_name', 'company']]
References
https://www.shanelynn.ie/select-pandas-dataframe-rows-and-columns-using-iloc-loc-and-ix/
https://thispointer.com/select-rows-columns-by-name-or-index-in-dataframe-using-loc-iloc-python-pandas/