Python Data Analysis Toolkit pandas

1. Brief introduction

Pandas is a high-performance, easy-to-use library of data structures and analysis tools designed for the Python programming language. It is built on top of NUMpy and can be seamlessly integrated into a scientific computing environment for many third-party libraries. Pandas is widely used in finance, statistics, social sciences, and many engineering and technical fields to handle typical data analysis cases.

2. Install

Pandas can be installed in conda and PIP modes.

Conda installation:

conda install pandas
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PIP installation:

pip install pandas
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As of this writing, the latest official release is V0.25.1, which was released on August 22, 2019. The latest version is a bug-fixed version of the 0.25.x series. The update mode is as follows:

pip install --upgrade pandas
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3. Data structure

Pandas has two main data structures: Series (dimension 1) and DataFrame (dimension 2).

The following describes both data structures separately, starting with importing pandas, abbreviated to PD by industry convention, into our Python script or Jupyter Notebook.

import pandas as pd
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3.1 Series

Series is a one-dimensional token array that can hold any data type (integer, string, floating point, Python object, etc.). Axis labels are collectively referred to as indexes.

3.1.1 create Series

Create from a list:

data = [1.2.3]
pd.Series(data)
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0    1
1    2
2    3
dtype: int64
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Create from a dictionary:

data = {'a': 1.'b': 2.'c': 3}
pd.Series(data)
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a    1
b    2
c    3
dtype: int64
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Set the index (label) with the index parameter:

data = [1.2.3]
index = ['a'.'b'.'c']
pd.Series(data, index=index)
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a    1
b    2
c    3
dtype: int64
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Created by scalar (same value) and set index (label, cannot be repeated) :

data = 0
index = ['a'.'b'.'c']
pd.Series(data, index=index)
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a    0
b    0
c    0
dtype: int64
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Granted access to the Series

s = pd.Series([10.100.1000], index=['a'.'b'.'c'])
s
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a      10
b     100
c    1000
dtype: int64
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Array access:

print(s[0], s[1], s[2])
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10, 100, 1000Copy the code

Dictionary access:

print(s['a'], s['b'], s['c'])
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10, 100, 1000Copy the code

The corresponding relationship between the two access modes can be seen:

print(s[0] == s['a'], s[1] == s['b'], s[2] == s['c'])
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True True True
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3.2 DataFrame

A DataFrame is a two-dimensional tagged data structure with possibly different types of columns. It can be likened to a spreadsheet or SQL table, or a dictionary for a Series object. It is also the most commonly used object for pandas.

3.2.1 create DataFrame

Create from list dictionary:

data = {
    'col1': [1.2.3].'col2': [4.5.6].'col3': [7.8.9]
}

pd.DataFrame(data)
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Create from Series dictionary:

s1 = pd.Series([1.2.3], index=['row1'.'row2'.'row3'])
s2 = pd.Series([4.5.6], index=['row2'.'row3'.'row4'])
s3 = pd.Series([7.8.9], index=['row3'.'row4'.'row5'])

data = {
    'col1': s1,
    'col2': s2,
    'col3': s3
}

pd.DataFrame(data)
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Create from a dictionary list:

data = [
    {'col1': 1.'col2': 2.'col3': 3},
    {'col1': 2.'col2': 3.'col3': 4},
    {'col1': 3.'col2': 4.'col3': 5}
]

pd.DataFrame(data, index=['row1'.'row2'.'row3'])
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Create from a two-dimensional list:

data = [
    [1.2.3],
    [2.3.4],
    [3.4.5]
]

pd.DataFrame(data, index=['row1'.'row2'.'row3'], columns=['col1'.'col2'.'col3'])
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3.2.2 access DataFrame

df = pd.DataFrame([[1.4.7], [2.5.8], [3.6.9]],
                  index=['row1'.'row2'.'row3'],
                  columns=['col1'.'col2'.'col3'])
df
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Access a column through a column label:

df['col1']
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row1    1
row2    2
row3    3
Name: col1, dtype: int64
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Access a row through a row label:

df.loc['row1']
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col1    1
col2    4
col3    7
Name: row1, dtype: int64
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Accessing a row via an integer:

df.iloc[0]
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col1    1
col2    4
col3    7
Name: row1, dtype: int64
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Select rows by slicing:

df[1:]
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3.2.3 transposed DataFrame

Transpose the rows and columns, similar to the transpose of a matrix in linear algebra.

df.T
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