Pandas data analysis (data structure) and pandas Data Analysis

Pandas data analysis (data structure) and pandas Data Analysis

This article mainly expands pandas data structures in the following two directions: Series and DataFrame (corresponding to one-dimensional arrays and two-dimensional arrays in Series and numpy)

1. First, we will introduce how to create a Series.

1) A sequence can be created using an array.

For example, test in python3.6.

# First, import two modules,

Import numpy as np

Import pandas as pd

# Create the first sequence

Array1 = np. arange (10)

Print (array1)

Print (type (array1 ))

#[0 1 2 3 4 5 6 7 8 9]

# <Class 'numpy. ndarray'>

S1 = pd. Series (array1)

Print (s1)

Print (type (s1 ))

Through the above, you can easily create a Series

As follows:

####

0 0
1 1
2 2
3 3
4
5 5
6 6
7
8
9 9
Dtype: int32

#######

The first column is the index column of Series, and the second column is an array from 0 to 9.

Dtype is the data type of Series (we will introduce the Data Type in pandas later)

2) We can also create Series in the form of dictionaries.

Dict1 = {'A': 10, 'B': 20, 'C': 30, 'D': 40, 'E': 50}

S2 = pd. Series (dict1)

Print (s2)

####

A 10
B 20
C 30
D 40
E 50
Dtype: int64

####

After the dictionary is passed into Series, the dictionary's key-value pairs are converted into index columns and data columns of Series respectively.