Note

Click here to download the full example code

12.3.10.4.8. Create and view an objectΒΆ

import pandas as pd
import numpy as np

Create an object

series = pd.Series([1, 3, 5, np.nan, 6, 8])

dates = pd.date_range("20220501", periods=6)

dataFrame = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD"))

dataFrame2 = pd.DataFrame(
    {
        "A": 1.0,
        "B": pd.Timestamp("20220501"),
        "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",
    }
)

View an object

dataFrame2.head()
A B C D E F
0 1.0 2022-05-01 1.0 3 test foo
1 1.0 2022-05-01 1.0 3 train foo
2 1.0 2022-05-01 1.0 3 test foo
3 1.0 2022-05-01 1.0 3 train foo 


dataFrame2.tail()
A B C D E F
0 1.0 2022-05-01 1.0 3 test foo
1 1.0 2022-05-01 1.0 3 train foo
2 1.0 2022-05-01 1.0 3 test foo
3 1.0 2022-05-01 1.0 3 train foo 


dataFrame2.dtypes

A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

dataFrame2.index

Int64Index([0, 1, 2, 3], dtype='int64')

dataFrame2.columns

Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')

dataFrame2.describe()
A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0


Convert to numpy

dataFrame.to_numpy()

array([[ 2.51194555, -1.80634016, -0.63091148,  0.57827393],
       [-0.15524733, -0.92884337, -0.67329603, -0.16581944],
       [ 0.13049717,  0.39749269, -0.02893293,  1.14751273],
       [ 0.77122503, -0.74610714,  1.02496218,  0.06675625],
       [ 2.0653632 ,  1.08194213, -0.13582842,  0.50800546],
       [-1.08819937,  1.69327597,  0.28704203, -0.38299148]])

Transpose, sorting data

dataFrame.T
2022-05-01 2022-05-02 2022-05-03 2022-05-04 2022-05-05 2022-05-06
A 2.511946 -0.155247 0.130497 0.771225 2.065363 -1.088199
B -1.806340 -0.928843 0.397493 -0.746107 1.081942 1.693276
C -0.630911 -0.673296 -0.028933 1.024962 -0.135828 0.287042
D 0.578274 -0.165819 1.147513 0.066756 0.508005 -0.382991 


dataFrame.sort_index(axis=1, ascending=False)
D C B A
2022-05-01 0.578274 -0.630911 -1.806340 2.511946
2022-05-02 -0.165819 -0.673296 -0.928843 -0.155247
2022-05-03 1.147513 -0.028933 0.397493 0.130497
2022-05-04 0.066756 1.024962 -0.746107 0.771225
2022-05-05 0.508005 -0.135828 1.081942 2.065363
2022-05-06 -0.382991 0.287042 1.693276 -1.088199 


dataFrame.sort_values(by="B")
A B C D
2022-05-01 2.511946 -1.806340 -0.630911 0.578274
2022-05-02 -0.155247 -0.928843 -0.673296 -0.165819
2022-05-04 0.771225 -0.746107 1.024962 0.066756
2022-05-03 0.130497 0.397493 -0.028933 1.147513
2022-05-05 2.065363 1.081942 -0.135828 0.508005
2022-05-06 -1.088199 1.693276 0.287042 -0.382991


Total running time of the script: ( 0 minutes 0.033 seconds)