pandas loc and iloc code example

Example 1: iloc in dataframe

df=pd.read_csv('yourcsv.csv')
X=df.iloc[:,:-1].values
y=df.iloc[:,1].values

Example 2: loc and iloc in pandas

iloc - default indexes (system generated)
loc - table indexes or we manually given indexes

Example 3: pandas df by row index

indices = [133, 22, 19, 203, 14, 1]
df_by_indices = df.iloc[indices, :]

Example 4: iloc pandas

Purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Example 5: pandas loc iloc

# Selecting Datafrmae Information:
# iloc

# selecting a single row:
df.iloc[3]

# selecting a range of rows:
df.iloc[0:3]

# selecting all rows, with columns within an index range:
# all rows, 1st- 3rd columns, sliced at second index:
df.iloc[:, 0:3]

# selecting a range of rows and a range of columns:
# 1st to 3rd rows, 5th & 6th columns:
df.iloc[0:3, 4:6]

# by multiple noconsecutive rows and columns:
# selecting rows 1, 4, 6 with columns 2, 3, 5:
df.iloc[[0, 3, 5], [1, 2, 4]]


# a) .loc label-based indexing- selecting columns based on index:
# all rows:
df.loc[:, 'column_name']

# or:
df['column_name']

# selected rows:
df.loc[0:5, 'column_name']

# b) boolean indexing using .loc:
df.loc[df['column_name'] < 5]

#boolean indexing fro one column:
df.loc[df['column_condition'] < 12, ['column_desired']]

Example 6: how to use loc and iloc in pandas

>>> df.iloc[0, 1]
2