3.8. Series Alter¶
3.8.1. Drop Rows¶
Drop element at index
Works with
inplace=True
import pandas as pd
s = pd.Series([1.0, 2.0, 3.0, None, 5.0])
s.drop(1)
# 0 1.0
# 2 3.0
# 3 NaN
# 4 5.0
# dtype: float64
s.drop([0,2,4])
# 1 2.0
# 3 NaN
# dtype: float64
3.8.2. Drop Duplicates¶
Works with
inplace=True
import pandas as pd
s = pd.Series([1.0, 2.0, 2.0, None, 5.0])
s.drop_duplicates()
# 0 1.0
# 1 2.0
# 3 NaN
# 4 5.0
# dtype: float64
3.8.3. Reset Index¶
Works with
inplace=True
drop=True
prevents the old index being added as a column
import pandas as pd
s = pd.Series([1.0, 2.0, 3.0, None, 5.0])
s.drop([0,1], inplace=True)
# 2 3.0
# 3 NaN
# 4 5.0
# dtype: float64
s.reset_index()
# index 0
# 0 2 3.0
# 1 3 NaN
# 2 4 5.0
s.reset_index(drop=True, inplace=True)
# 0 3.0
# 1 NaN
# 2 5.0
# dtype: float64
3.8.4. Assignments¶
"""
* Assignment: Series Alter
* Complexity: easy
* Lines of code: 10 lines
* Time: 5 min
English:
1. Use data from "Given" section (see below)
2. From input data create `s: pd.Series`
3. Drop values at index 2, 4, 6
4. Drop duplicates
5. Reindex series (without old copy)
6. Print series
Polish:
1. Użyj danych z sekcji "Given" (patrz poniżej)
2. Z danych wejściowych stwórz `s: pd.Series`
3. Usuń wartości na indeksach 2, 4, 6
4. Usuń duplikujące się wartości
5. Zresetuj indeks (bez kopii starego)
6. Wypisz serię
Tests:
>>> type(result) is pd.Series
True
>>> result
0 1.0
1 NaN
2 2.0
dtype: float64
"""
# Given
import pandas as pd
DATA = [1, None, 5, None, 1, 2, 1]
result = ...