Python is a very good language for data analysis, mainly because data-centric libraries are very suitable. Pandas is one of them, making it easier to import and analyze data. In this article, I used to analyze the data in the Country Data.csv file in the public data set on the Stanford website.
Installation
Install Pandas:
pip install pandasCreate a DataFrame in Pandas by using the pd.Series method to pass multiple Series to the DataFrame class to complete the creation of the data frame. Here, it is passed in two Series objects, with s1 as the first row and s2 as the second row.
example:
Output:
Import data with Pandas
The first step is to read the data. The data is stored as a comma-separated value or csv file, where each row is separated by a newline, and each column is separated by a comma (,). In order to be able to use the data in Python, the csv file needs to be read into a Pandas DataFrame. DataFrame is a way to represent and process table data.
example:
import pandas as pd
df = pd.read_csv("IND_data.csv")
df.head()
df.shape Output:
29,10Index DataFrames with Pandas
You can use the pandas.DataFrame.iloc method to build an index. The iloc method allows retrieval of up to rows and columns by location.
example:
df.iloc[0:5,:]
df.iloc[:,:]
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Indexing using tags in Pandas
You can use the pandas.DataFrame.loc method to index labels, which allows you to use labels instead of positions for indexing.
example:
df.loc[0:5,:]
df = df.loc[5:,:] The above content is actually not much different from df.iloc [0:5,:]. This is because although the row label can take any value, our row label matches the position exactly. However, column labels can make processing data easier. example:
df.loc[:5,"Time period"]
DataFrame Math与Pandas
The calculation of the data frame can be done by using the statistical function of the pandas tool.
example:
df.describe()
df.corr()
df.rank()
Pandas diagram
The graphs in these examples are made using standard conventions for referencing the matplotlib API, which provides the basics of Pandas to easily create beautiful maps.
example:
import the required module
import matplotlib.pyplot as plt
df['Observation Value'].hist(bins=10)
df.boxplot(column='Observation Value', by = 'Time period')
x = df["Observation Value"]
y = df["Time period"]
plt.scatter(x, y, label= "stars", color= "m",
marker= "*", s=30)
plt.xlabel('Observation Value')
plt.ylabel('Time period')
plt.show()
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