Lettura, ispezione e pulizia di dati da CSV

Importing and Managing Financial Data in Python

Stefan Jansen

Instructor

Importare e pulire i dati

Assicurati che pd.DataFrame() coincida con il file CSV sorgente
Quotazioni di borsa: amex-listings.csv

Dati AmEx

Come pandas archivia i dati

Ogni colonna ha un proprio formato (dtype)
Il dtype influenza calcoli e visualizzazioni

`pandas` `dtype`	Caratteristiche colonna
`object`	Testo o mix di testo e numeri
`int64`	Numerico: interi a 64 bit ($\le 2^{64}$)
`float64`	Numerico: decimali o interi con valori mancanti
`datetime64`	Data e ora

Importare e ispezionare

import pandas as pd

amex = pd.read_csv('amex-listings.csv')

amex.info() # Per ispezionare struttura tabella e tipi

RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
 #   Column                 Non-Null Count  Dtype  
 --  ------                 --------------  -----  
 0   Stock Symbol           360 non-null    object 
 1   Company Name           360 non-null    object 
 2   Last Sale              346 non-null    float64
 3   Market Capitalization  360 non-null    float64
 4   IPO Year               105 non-null    float64
 5   Sector                 238 non-null    object 
  6   Industry               238 non-null    object 
 7   Last Update            360 non-null    object 
dtypes: float64(3), object(5)

Gestire i valori mancanti

# Sostituisci 'n/a' con np.nan
amex = pd.read_csv('amex-listings.csv', na_values='n/a')

amex.info()

RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
 #   Column                 Non-Null Count  Dtype  
 --  ------                 --------------  -----  
 0   Stock Symbol           360 non-null    object 
 1   Company Name           360 non-null    object 
 2   Last Sale              346 non-null    float64
 3   Market Capitalization  360 non-null    float64
 4   IPO Year               105 non-null    float64
 5   Sector                 238 non-null    object 
 6   Industry               238 non-null    object 
 7   Last Update            360 non-null    object 
dtypes: float64(3), object(5)

Parsing corretto delle date

amex = pd.read_csv('amex-listings.csv',
                   na_values='n/a',
                   parse_dates=['Last Update'])

amex.info()

RangeIndex: 360 entries, 0 to 359
Data columns (total 8 columns):
 #   Column                 Non-Null Count  Dtype         
 --  ------                 --------------  -----  
 0   Stock Symbol           360 non-null    object        
 1   Company Name           360 non-null    object        
 2   Last Sale              346 non-null    float64       
 3   Market Capitalization  360 non-null    float64       
 4   IPO Year               105 non-null    float64       
 5   Sector                 238 non-null    object        
 6   Industry               238 non-null    object        
 7   Last Update            360 non-null    datetime64[ns]
dtypes: datetime64[ns](1), float64(3), object(4)

Mostrare il risultato

amex.head(2) # Mostra le prime n righe (default: 5)

  Stock Symbol   Company Name  
0         XXII   22nd Century Group, Inc   
1          FAX   Aberdeen Asia-Pacific Income Fund Inc   

   Last Sale  Market Capitalization  IPO Year  
0     1.3300           1.206285e+08       NaN  
1     5.0000           1.266333e+09    1986.0  

   Sector        Industry              Last Update  
0  Non-Durables  Farming/Seeds/Milling  2017-04-26  
1  NaN           NaN                    2017-04-25

Vamos praticar!

Importing and Managing Financial Data in Python