Learning notes | Data Analysis: 1.1 data evaluation

| Data Evaluation |

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import csv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline

　　# Import/load the data set use the read_csv function of Pandas

Shanghai_data = pd.read_csv('ShanghaiPM20100101_20151231.csv')

　　# View the basic information of data by means of head, info and describe.

Shanghai_data.head()
Shanghai_data.info()

　　# Print type of python object

print(type(Shanghai_data['cbwd'][0]))

　　# Change the space into an underline

Shanghai_data.columns = [c.replace(' ', '_') for c in Shanghai_data.columns]

　　# Convert the numerical value of 1, 2, 3, 4 to four corresponding seasons (by means of the map method of Pandas):

Shanghai_data['season'] = Shanghai_data['season'].map({1:'Spring', 2:'Summer', 3:'Autumn', 4: 'Winter'})

- Check data missing and data type:

　　# Print the length of data

print("The number of row in this dataset is ",len(Shanghai_data.index))

　　# Calculating the number of records in column "PM_Jingan"

print("There number of missing data records in PM_Jingan is: ",len(Shanghai_data.index) - len(Shanghai_data['PM_Jingan'].dropna()))

Note: # “dropna()” function used in the following code can delete missing value in data.