4.8. DataFrame Loc¶
4.8.1. Rationale¶
loc- uses fancy indexingiloc- only index numbersdf.loc- start and stop are included!!df.iloc- behaves like Python slices
Figure 4.2. Pandas Select Row¶
Figure 4.3. Pandas Select Cell¶
4.8.2. Data¶
import pandas as pd
import numpy as np
np.random.seed(0)
df = pd.DataFrame(
columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
index = pd.date_range('1999-12-30', periods=7),
data = np.random.randn(7, 4))
df
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.978738 2.240893
# 1999-12-31 1.867558 -0.977278 0.950088 -0.151357
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-02 0.761038 0.121675 0.443863 0.333674
# 2000-01-03 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
# 2000-01-05 2.269755 -1.454366 0.045759 -0.187184
4.8.3. All Values in Row¶
4.8.4. Single row¶
Returns the row as a
pd.Series
df.loc['2000-01-01']
# Morning -0.103219
# Noon 0.410599
# Evening 0.144044
# Midnight 1.454274
# Name: 2000-01-01 00:00:00, dtype: float64
4.8.5. Range of rows¶
Returns the rows as a
pd.DataFrame
df.loc['2000-01-02':'2000-01-04']
# Morning Noon Evening Midnight
# 2000-01-02 0.761038 0.121675 0.443863 0.333674
# 2000-01-03 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
4.8.6. Range of dates¶
df.loc['2000-01']
# Morning Noon Evening Midnight
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-02 0.761038 0.121675 0.443863 0.333674
# 2000-01-03 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
# 2000-01-05 2.269755 -1.454366 0.045759 -0.187184
df.loc['1999']
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.978738 2.240893
# 1999-12-31 1.867558 -0.977278 0.950088 -0.151357
4.8.7. Values in Selected Columns¶
Note that both the start and stop of the slice are included
Single row and single column:
df.loc['2000-01-05', 'Morning']
# 2.2697546239876076
Range of rows and single column:
df.loc['1999-12-31':'2000-01-02', 'Noon']
# 1999-12-31 -0.977278
# 2000-01-01 0.410599
# 2000-01-02 0.121675
# Freq: D, Name: Noon, dtype: float64
Range of rows and single column:
df.loc[['2000-01-02','2000-01-04'], 'Noon']
# 2000-01-02 0.121675
# 2000-01-04 0.653619
# Name: Noon, dtype: float64
Single row and selected columns:
df.loc['2000-01-05', ['Noon', 'Midnight']]
# Noon -1.454366
# Midnight -0.187184
# Name: 2000-01-05 00:00:00, dtype: float64
Single row and column range:
df.loc['2000-01-05', 'Noon':'Midnight']
# Noon -1.454366
# Evening 0.045759
# Midnight -0.187184
# Name: 2000-01-05 00:00:00, dtype: float64
4.8.8. Fancy Indexing¶
Return row for given index is
True
Boolean list with the same length as the row axis:
df.loc[[True, False, True, False, False, False, True]]
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.978738 2.240893
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-05 2.269755 -1.454366 0.045759 -0.187184
Conditional that returns a boolean Series:
df.loc[df['Morning'] < 0]
# Morning Noon Evening Midnight
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
Conditional that returns a boolean Series with column labels specified:
df.loc[df['Morning'] < 0, 'Evening']
# 2000-01-01 0.144044
# 2000-01-04 0.864436
# Freq: 3D, Name: Evening, dtype: float64
df.loc[df['Morning'] < 0, ['Morning', 'Evening']]
# Morning Evening
# 2000-01-01 -0.103219 0.144044
# 2000-01-04 -2.552990 0.864436
where = df['Morning'] < 0
df.loc[where, ['Morning', 'Evening']]
# Morning Evening
# 2000-01-01 -0.103219 0.144044
# 2000-01-04 -2.552990 0.864436
where = df['Morning'] < 0
select = ['Morning', 'Evening']
df.loc[where, select]
# Morning Evening
# 2000-01-01 -0.103219 0.144044
# 2000-01-04 -2.552990 0.864436
4.8.9. Callable¶
Filtering with callable:
def morning_below_zero(df):
return df['Morning'] < 0
df.loc[morning_below_zero]
# Morning Noon Evening Midnight
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
df.loc[lambda df: df['Morning'] < 0]
# Morning Noon Evening Midnight
# 2000-01-01 -0.103219 0.410599 0.144044 1.454274
# 2000-01-04 -2.552990 0.653619 0.864436 -0.742165
4.8.10. Setting Values¶
Set value for all items matching the list of labels:
df.loc[df['Morning'] < 0, 'Evening'] = np.inf
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.978738 2.240893
# 1999-12-31 1.867558 -0.977278 0.950088 -0.151357
# 2000-01-01 -0.103219 0.410599 inf 1.454274
# 2000-01-02 0.761038 0.121675 0.443863 0.333674
# 2000-01-03 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-04 -2.552990 0.653619 inf -0.742165
# 2000-01-05 2.269755 -1.454366 0.045759 -0.187184
Set value for an entire row:
df.loc['2000-01-01'] = np.nan
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.978738 2.240893
# 1999-12-31 1.867558 -0.977278 0.950088 -0.151357
# 2000-01-01 NaN NaN NaN NaN
# 2000-01-02 0.761038 0.121675 0.443863 0.333674
# 2000-01-03 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-04 -2.552990 0.653619 inf -0.742165
# 2000-01-05 2.269755 -1.454366 0.045759 -0.187184
Set value for an entire column:
df.loc[:, 'Evening'] = 0.0
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.0 2.240893
# 1999-12-31 1.867558 -0.977278 0.0 -0.151357
# 2000-01-01 NaN NaN 0.0 NaN
# 2000-01-02 0.761038 0.121675 0.0 0.333674
# 2000-01-03 1.494079 -0.205158 0.0 -0.854096
# 2000-01-04 -2.552990 0.653619 0.0 -0.742165
# 2000-01-05 2.269755 -1.454366 0.0 -0.187184
Set value for rows matching callable condition:
df[df < 0] = -np.inf
df
# Morning Noon Evening Midnight
# 1999-12-30 1.764052 0.400157 0.0 2.240893
# 1999-12-31 1.867558 -inf 0.0 -inf
# 2000-01-01 NaN NaN 0.0 NaN
# 2000-01-02 0.761038 0.121675 0.0 0.333674
# 2000-01-03 1.494079 -inf 0.0 -inf
# 2000-01-04 -inf 0.653619 0.0 -inf
# 2000-01-05 2.269755 -inf 0.0 -inf
4.8.11. Assignments¶
Todo
Create assignments