Pandas Learning Notes

Programming Languages | 26 June 2019

Pandas is an open-source scientific computing package for Python programming language which provides high-performance, easy to use data structures and data analysis tools to work with data. Some of the domains where Pandas is used are Deep Learning, Computer Vision, Machine Learning, Image Processing, Data Analytics, Spreadsheet manipulation etc.

In this page, you will find the syntax and most common how to’s of Pandas in Python. This might be useful for python beginners to find syntax and code quickly online in a single page.

Update: As Python2 faces end of life, the below code only supports Python3.

Check pandas versioncode

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import pandas as pd 
print(pd.__version__)
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'0.24.2'

Load toy dataset to work with pandascode

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# please install scikit-learn - https://scikit-learn.org/ to use this dataset
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
print(cancer.DESCR)
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Breast Cancer Wisconsin (Diagnostic) Database
=============================================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 569

    :Number of Attributes: 30 numeric, predictive attributes and the class

    :Attribute Information:
        - radius (mean of distances from center to points on the perimeter)
        - texture (standard deviation of gray-scale values)
        - perimeter
        - area
        - smoothness (local variation in radius lengths)
        - compactness (perimeter^2 / area - 1.0)
        - concavity (severity of concave portions of the contour)
        - concave points (number of concave portions of the contour)
        - symmetry 
        - fractal dimension ("coastline approximation" - 1)

        The mean, standard error, and "worst" or largest (mean of the three
        largest values) of these features were computed for each image,
        resulting in 30 features.  For instance, field 3 is Mean Radius, field
        13 is Radius SE, field 23 is Worst Radius.

        - class:
                - WDBC-Malignant
                - WDBC-Benign

    :Summary Statistics:

    ===================================== ====== ======
                                           Min    Max
    ===================================== ====== ======
    radius (mean):                        6.981  28.11
    texture (mean):                       9.71   39.28
    perimeter (mean):                     43.79  188.5
    area (mean):                          143.5  2501.0
    smoothness (mean):                    0.053  0.163
    compactness (mean):                   0.019  0.345
    concavity (mean):                     0.0    0.427
    concave points (mean):                0.0    0.201
    symmetry (mean):                      0.106  0.304
    fractal dimension (mean):             0.05   0.097
    radius (standard error):              0.112  2.873
    texture (standard error):             0.36   4.885
    perimeter (standard error):           0.757  21.98
    area (standard error):                6.802  542.2
    smoothness (standard error):          0.002  0.031
    compactness (standard error):         0.002  0.135
    concavity (standard error):           0.0    0.396
    concave points (standard error):      0.0    0.053
    symmetry (standard error):            0.008  0.079
    fractal dimension (standard error):   0.001  0.03
    radius (worst):                       7.93   36.04
    texture (worst):                      12.02  49.54
    perimeter (worst):                    50.41  251.2
    area (worst):                         185.2  4254.0
    smoothness (worst):                   0.071  0.223
    compactness (worst):                  0.027  1.058
    concavity (worst):                    0.0    1.252
    concave points (worst):               0.0    0.291
    symmetry (worst):                     0.156  0.664
    fractal dimension (worst):            0.055  0.208
    ===================================== ====== ======

    :Missing Attribute Values: None

    :Class Distribution: 212 - Malignant, 357 - Benign

    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

    :Donor: Nick Street

    :Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass.  They describe
characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree.  Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

References
----------
   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction 
     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on 
     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
     San Jose, CA, 1993.
   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and 
     prognosis via linear programming. Operations Research, 43(4), pages 570-577, 
     July-August 1995.
   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 
     163-171.

How to create a DataFrame?code

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X = cancer["data"]
y = np.expand_dims(cancer["target"], axis=1)
index   = range(0, 569, 1)
columns = [ 'mean radius', 'mean texture', 'mean perimeter', 'mean area',
            'mean smoothness', 'mean compactness', 'mean concavity',
            'mean concave points', 'mean symmetry', 'mean fractal dimension',
            'radius error', 'texture error', 'perimeter error', 'area error',
            'smoothness error', 'compactness error', 'concavity error',
            'concave points error', 'symmetry error', 'fractal dimension error',
            'worst radius', 'worst texture', 'worst perimeter', 'worst area',
            'worst smoothness', 'worst compactness', 'worst concavity',
            'worst concave points', 'worst symmetry', 'worst fractal dimension',
            'target' ]
cancerdf = pd.DataFrame(np.concatenate((X, y), axis=1), index=index, columns=columns)
print(cancerdf.shape)
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(569, 31)

How to view head of a DataFrame?code

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cancerdf.head()
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   mean radius  mean texture  mean perimeter  ...  worst symmetry  worst fractal dimension  target
0        17.99         10.38          122.80  ...          0.4601                  0.11890     0.0
1        20.57         17.77          132.90  ...          0.2750                  0.08902     0.0
2        19.69         21.25          130.00  ...          0.3613                  0.08758     0.0
3        11.42         20.38           77.58  ...          0.6638                  0.17300     0.0
4        20.29         14.34          135.10  ...          0.2364                  0.07678     0.0

[5 rows x 31 columns]

How to count unique values in a DataFrame column?code

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count_malignant = cancerdf["target"].loc[cancerdf["target"]==0.0].count()
count_benign    = cancerdf["target"].loc[cancerdf["target"]==1.0].count()

# create a pandas series
target = pd.Series([count_malignant, count_benign], index=["malignant", "benign"])
print(target)
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malignant    212
benign       357
dtype: int64

How to split a dataframe?code

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X = cancerdf.iloc[:, :cancerdf.shape[1]-1]
y = cancerdf.iloc[:,  cancerdf.shape[1]-1]
print(X.shape)
print(y.shape)
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(569, 30)
(569,)

References

In case if you found something useful to add to this article or you found a bug in the code or would like to improve some points mentioned, feel free to write it down in the comments. Hope you found something useful here.