Time series data analysis skills in Python

With the continuous development of the data era, data analysis skills have become a basic quality for practitioners in various industries. In the process of data analysis, time series data analysis skills are particularly important. As one of the hottest programming languages ​​at present, Python is also widely used in the field of time series data analysis. This article will introduce some commonly used Python time series data analysis techniques to help readers analyze and process large-scale time series data more efficiently.

1. Introduction to data types

Time series data is a collection of data arranged in time order, such as daily weather temperature, stock prices, population, website clicks and other data. In Python, we can use the Pandas library and the Numpy library for time series data analysis and processing. The most commonly used data structures in Pandas are Series and DataFrame, where Series is a one-dimensional array used to store one column of data; DataFrame is a two-dimensional table data structure that can be used to store multiple columns of data.

2. Data loading

Before analysis, we first need to obtain data from the outside and then load the data. The Pandas and Numpy libraries in Python provide multiple ways to read data in various formats. For example, read data in CSV format:

import pandas as pd

data = pd.read_csv('data.csv')

In addition, the Pandas library also provides the to_csv method of DataFrame, which can output data into a CSV format file.

data.to_csv('data.csv')

3. Data Cleaning

Data cleaning is an essential step in data analysis. It includes removing dirty data and empty data, unifying data types, verifying data, etc. In time series data analysis, data cleaning may also require operations such as interpolation and feature selection. In Python, we can use the dropna method provided by Pandas to delete missing data.

data = data.dropna()

In addition, for time series data, non-stationary data samples may lead to some adverse consequences. For example, the data may show a seasonal trend, or an epidemic may occur because the data approaches a certain value. At this time, we can use Pandas' rolling method to perform rolling average to stabilize the time series data.

rolling_data = data.rolling(window=8, center=False).mean()

4. Data Analysis

For time series data analysis, we need to perform periodic analysis on the data to understand the periodic trend of the data. In Python, we can use the fft method to perform Fourier transform on the data and obtain the frequency and amplitude of the data.

import numpy as np

Fs = 1000   #采样频率
Ts = 1.0 / Fs #采样周期
L = 1500   #数据长度
t = np.linspace(0.0, L*Ts, L, endpoint=False)
data = np.sin(10*np.pi*t) + 0.5*np.sin(50*np.pi*t)

N = len(data)
yf = np.fft.fft(data)
xf = np.linspace(0.0, 1.0/(2.0*Ts), N/2)

import matplotlib.pyplot as plt

plt.plot(xf, 2.0/N * np.abs(yf[0:N/2]))
plt.grid()
plt.show()

5. Data Visualization

Data visualization is an important part of time series data analysis. It can display the data in front of us and help us better understand and gain insight into the data. There are several visualization tools available in Python, such as libraries such as Matplotlib and Seaborn. We can use these tools to visualize time series data, such as drawing time series plots, box plots, histograms, etc.

import matplotlib.pyplot as plt
import seaborn as sns

# 时间序列图
sns.lineplot(x="year", y="volume_sold", data=df)

# 箱形图
sns.boxplot(x="day", y="tip", data=tips)

# 直方图
sns.distplot(df["age"])

6. Conclusion

Time series data analysis involves many aspects such as data loading, data cleaning, data analysis and data visualization. In Python, we can use libraries such as Pandas and Numpy to Complete processing and analysis of date and time series data. Using Python for time series data analysis can help data analysts better grasp the dynamic changes and trends of data, so as to formulate corresponding data analysis and processing plans more efficiently.

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