4.12. DataFrame Alter¶
4.12.1. Add Rows and Columns¶
Add Column:
import pandas as pd
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']
# 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 does not match length of index
df['X'] = ['a', 'b', 'c', 'd']
# Traceback (most recent call last):
# ValueError: Length of values does not match length of index
df['Z'] = np.arange(3.0)
# A B C Z
# 0 10 20 30 0.0
# 1 11 21 31 1.0
# 2 12 22 32 2.0
4.12.2. Drop Rows and Columns¶
Works with
inplace=True
Drop Column:
import pandas as pd
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:
import pandas as pd
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 = df1[:2].index
df.drop(rows)
# A B C
# 2 12 22 32
Drop from Timeseries:
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
df.drop('1999-12-30')
# Traceback (most recent call last):
# KeyError: "['1999-12-30'] not found in axis"
df.drop(pd.Timestamp('1999-12-30'))
# 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
4.12.3. Transpose¶
df.transpose()ordf.Tdf.transpose()is preferred
import pandas as pd
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
import pandas as pd
df = pd.DataFrame({
'A': [10, 11, 12],
'B': [20, 21, 22],
'C': [30, 31, 32]})
df['A'] # will select column A
df['B'] # will select column B
df['C'] # will select column C
df.A # will select column A
df.B # will select column B
df.C # will select column C
df.T # will transpose data
df.transpose() # will transpose data
import pandas as pd
df = pd.DataFrame({
'R': [10, 11, 12],
'S': [20, 21, 22],
'T': [30, 31, 32]})
df['R'] # will select column R
df['S'] # will select column S
df['T'] # will select column T
df.R # will select column R
df.S # will select column S
df.T # will transpose data
df.transpose() # will transpose data