4.12. DataFrame Alter¶
pd.date_range('1999-12-30', periods=7)df['not-existing'] = 1df['not-existing'] = range(0,10)df['not-existing'] = np.arange(0,10).transpose().drop(columns=[...]).drop(index=[...]).drop_duplicates()
4.12.1. SetUp¶
>>> import pandas as pd
>>> import numpy as np
4.12.2. Add Rows and Columns¶
Add Column:
>>> df = pd.DataFrame({
... 'A': [10, 11, 12],
... 'B': [20, 21, 22],
... 'C': [30, 31, 32]})
>>>
>>> df
A B C
0 10 20 30
1 11 21 31
2 12 22 32
>>> df['X'] = ['a', 'b', 'c']
>>> df
A B C X
0 10 20 30 a
1 11 21 31 b
2 12 22 32 c
>>> df['X'] = ['a', 'b']
Traceback (most recent call last):
ValueError: Length of values (2) does not match length of index (3)
>>> df['X'] = ['a', 'b', 'c', 'd']
Traceback (most recent call last):
ValueError: Length of values (4) does not match length of index (3)
>>> df['Z'] = np.arange(3.0)
>>> df
A B C X Z
0 10 20 30 a 0.0
1 11 21 31 b 1.0
2 12 22 32 c 2.0
4.12.3. Drop Rows and Columns¶
Works with
inplace=True
Drop Column:
>>> df = pd.DataFrame({
... 'A': [10, 11, 12],
... 'B': [20, 21, 22],
... 'C': [30, 31, 32]})
>>>
>>> df
A B C
0 10 20 30
1 11 21 31
2 12 22 32
>>> df.drop('A', axis='columns')
B C
0 20 30
1 21 31
2 22 32
>>> df.drop(columns='A')
B C
0 20 30
1 21 31
2 22 32
>>> df.drop(columns=['A', 'B'])
C
0 30
1 31
2 32
Drop Row:
>>> df = pd.DataFrame({
... 'A': [10, 11, 12],
... 'B': [20, 21, 22],
... 'C': [30, 31, 32]})
>>>
>>> df
A B C
0 10 20 30
1 11 21 31
2 12 22 32
>>> df.drop(1)
A B C
0 10 20 30
2 12 22 32
>>> df.drop([0, 2])
A B C
1 11 21 31
>>> rows = df[:2].index
>>> df.drop(rows)
A B C
2 12 22 32
Drop from Timeseries:
>>> 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
>>> df.drop('1999-12-30')
Morning Noon Evening Midnight
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
4.12.4. Transpose¶
df.transpose()ordf.Tdf.transpose()is preferred
>>> df = pd.DataFrame({
... 'A': [10, 11, 12],
... 'B': [20, 21, 22],
... 'C': [30, 31, 32]})
>>>
>>> df
A B C
0 10 20 30
1 11 21 31
2 12 22 32
>>>
>>> df.transpose()
0 1 2
A 10 11 12
B 20 21 22
C 30 31 32
>>>
>>> df.T
0 1 2
A 10 11 12
B 20 21 22
C 30 31 32
>>> df = pd.DataFrame({
... 'A': [10, 11, 12],
... 'B': [20, 21, 22],
... 'C': [30, 31, 32]})
>>>
>>> x = df['A'] # will select column A
>>> x = df['B'] # will select column B
>>> x = df['C'] # will select column C
>>>
>>> x = df.A # will select column A
>>> x = df.B # will select column B
>>> x = df.C # will select column C
>>>
>>> x = df.T # will transpose data
>>> x = df.transpose() # will transpose data
>>> df = pd.DataFrame({
... 'R': [10, 11, 12],
... 'S': [20, 21, 22],
... 'T': [30, 31, 32]})
>>>
>>> x = df['R'] # will select column R
>>> x = df['S'] # will select column S
>>> x = df['T'] # will select column T
>>>
>>> x = df.R # will select column R
>>> x = df.S # will select column S
>>> x = df.T # will transpose data
>>>
>>> x = df.transpose() # will transpose data