4.1. DataFrame Create¶
pd.DataFrame(list[dict])pd.DataFrame(dict[str,list])
4.1.1. SetUp¶
>>> import pandas as pd
>>> import numpy as np
4.1.2. Create from List of Dicts¶
>>> pd.DataFrame([
... {'A': 1.0, 'B': 2.0},
... {'A': 3.0, 'B': 4.0},
... ])
A B
0 1.0 2.0
1 3.0 4.0
>>> pd.DataFrame([
... {'A': 1.0, 'B': 2.0},
... {'B': 3.0, 'C': 4.0},
... ])
A B C
0 1.0 2.0 NaN
1 NaN 3.0 4.0
>>> pd.DataFrame([
... {'firstname': 'Mark', 'lastname': 'Watney'},
... {'firstname': 'Melissa', 'lastname': 'Lewis'},
... {'firstname': 'Rick', 'lastname': 'Martinez'},
... {'firstname': 'Alex', 'lastname': 'Vogel'},
... ])
firstname lastname
0 Mark Watney
1 Melissa Lewis
2 Rick Martinez
3 Alex Vogel
4.1.3. Create from Dict¶
>>> pd.DataFrame({
... 'A': ['a', 'b', 'c'],
... 'B': [1.0, 2.0, 3.0],
... 'C': [1, 2, 3],
... })
A B C
0 a 1.0 1
1 b 2.0 2
2 c 3.0 3
>>> pd.DataFrame({
... 'firstname': ['Mark', 'Melissa', 'Rick', 'Alex'],
... 'lastname': ['Watney', 'Lewis', 'Martinez', 'Vogel'],
... })
firstname lastname
0 Mark Watney
1 Melissa Lewis
2 Rick Martinez
3 Alex Vogel
4.1.4. Create from NDArray¶
>>> import pandas as pd
>>> import numpy as np
>>> np.random.seed(0)
>>>
>>>
>>> df = pd.DataFrame(np.random.randn(7, 4))
>>>
>>> df
0 1 2 3
0 1.764052 0.400157 0.978738 2.240893
1 1.867558 -0.977278 0.950088 -0.151357
2 -0.103219 0.410599 0.144044 1.454274
3 0.761038 0.121675 0.443863 0.333674
4 1.494079 -0.205158 0.313068 -0.854096
5 -2.552990 0.653619 0.864436 -0.742165
6 2.269755 -1.454366 0.045759 -0.187184
4.1.5. Use Case - 0x01¶
>>> import pandas as pd
>>> import numpy as np
>>>
>>>
>>> pd.DataFrame({
... 'A': 1.,
... 'B': pd.Timestamp('1961-04-12'),
... 'C': pd.Series(1, index=list(range(4)), dtype='float32'),
... 'D': np.array([3] * 4, dtype='int32'),
... 'E': pd.Categorical(["test", "train", "test", "train"]),
... 'F': 'foo',
... 'G': [1,2,3,4],
... })
A B C D E F G
0 1.0 1961-04-12 1.0 3 test foo 1
1 1.0 1961-04-12 1.0 3 train foo 2
2 1.0 1961-04-12 1.0 3 test foo 3
3 1.0 1961-04-12 1.0 3 train foo 4
4.1.6. Use Case - 0x02¶
>>> 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
4.1.7. Assignments¶
"""
* Assignment: DataFrame Create
* Complexity: easy
* Lines of code: 10 lines
* Time: 5 min
English:
1. Create `result: pd.DataFrame` for input data
2. Run doctests - all must succeed
Polish:
1. Stwórz `result: pd.DataFrame` dla danych wejściowych
2. Uruchom doctesty - wszystkie muszą się powieść
Hints:
* Use selection with `alt` key in your IDE
Tests:
>>> import sys; sys.tracebacklimit = 0
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)
>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is pd.DataFrame, \
'Variable `result` must be a `pd.DataFrame` type'
>>> result # doctest: +NORMALIZE_WHITESPACE
Crew Role Astronaut
0 Prime CDR Neil Armstrong
1 Prime LMP Buzz Aldrin
2 Prime CMP Michael Collins
3 Backup CDR James Lovell
4 Backup LMP William Anders
5 Backup CMP Fred Haise
"""
import pandas as pd
"""
"Prime", "CDR", "Neil Armstrong"
"Prime", "LMP", "Buzz Aldrin"
"Prime", "CMP", "Michael Collins"
"Backup", "CDR", "James Lovell"
"Backup", "LMP", "William Anders"
"Backup", "CMP", "Fred Haise"
"""
# type: pd.DataFrame
result = ...