col_names = ['STATEFIPS', 'STATE', 'zipcode', 'agi_stub', 'N1']
col_nums = [0, 1, 2, 3, 4]
# Choose columns to load by name
tax_data_v1 = pd.read_csv('us_tax_data_2016.csv', usecols=col_names)
# Choose columns to load by number
tax_data_v2 = pd.read_csv('us_tax_data_2016.csv',
usecols=col_nums)
print(tax_data_v1.equals(tax_data_v2))
True

tax_data_next500 = pd.read_csv('us_tax_data_2016.csv', nrows=500, skiprows=1000, header=None)

# tax_data_first1000 is dataframe
# extracting column name with list
col_names = list(tax_data_first1000)
tax_data_next500 = pd.read_csv('us_tax_data_2016.csv', nrows=500, skiprows=1000,header=None,names=col_names) print(tax_data_next500.head(1))

tax_data = pd.read_csv("us_tax_data_2016.csv", dtype={"zipcode": str})

tax_data = pd.read_csv("us_tax_data_2016.csv", na_values={"zipcode":0})
print(tax_data[tax_data.zipcode.isna()])

>>> tax_data = pd.read_csv("us_tax_data_2016_corrupt.csv", error_bad_lines=False, warn_bad_lines=True)
b'Skipping line 3: expected 147 fields, saw 148\n'

# Read columns W-AB and AR of file, skipping metadata header
survey_data = pd.read_excel("fcc_survey_with_headers.xlsx", skiprows=2, usecols="W:AB, AR")
# View data
print(survey_data.head())

# Get the second sheet by position index
survey_data_sheet2 = pd.read_excel('fcc_survey.xlsx', sheet_name=1)
# Get the second sheet by name
survey_data_2017 = pd.read_excel('fcc_survey.xlsx', sheet_name='2017')
>>> print(survey_data_sheet2.equals(survey_data_2017))
True

>>> survey_responses = pd.read_excel("fcc_survey.xlsx", sheet_name=None)
>>> print(type(survey_responses))
<class 'collections.OrderedDict'>

# key would be 2016, 2017 here
>> for key, value in survey_responses.items():
>>     print(key, type(value))
2016 <class 'pandas.core.frame.DataFrame'>
2017 <class 'pandas.core.frame.DataFrame'>		

# Load data, casting True/False columns as Boolean
bool_data = pd.read_excel("fcc_survey_booleans.xlsx",
                          dtype={
                            "AttendedBootcamp": bool,
                            "AttendedBootCampYesNo": bool,
                            "AttendedBootcampTF":bool,
                            "BootcampLoan": bool,
                            "LoanYesNo": bool,
                            "LoanTF": bool},
                          true_values=["Yes"], false_values=["No"])

# List columns of dates to parse
date_cols = ["Part1StartTime", "Part1EndTime"]

# Load file, parsing standard datetime columns
survey_df = pd.read_excel("fcc_survey.xlsx", parse_dates=date_cols)

# List columns of dates to parse
date_cols = ["Part1StartTime","Part1EndTime", [["Part2StartDate","Part2StartTime"]]]
# Load file, parsing standard and split datetime columns

survey_df = pd.read_excel("fcc_survey.xlsx", parse_dates=date_cols)
print(survey_df.head(3))

# List columns of dates to parse
date_cols = {"Part1Start":"Part1StartTime",
             "Part1End":"Part1EndTime","Part2Start": ["Part2StartDate","Part2StartTime"]}
# Load file, parsing standard and split datetime columns
survey_df = pd.read_excel("fcc_survey.xlsx", parse_dates=date_cols)
print(survey_df.Part2Start.head(3))

format_string = "%m%d%Y %H:%M:%S"
survey_df["Part2EndTime"] = pd.to_datetime(survey_df["Part2EndTime"], format=format_string)
print(survey_df.Part2EndTime.head())

import pandas as pd
death_causes = pd.read_json("nyc_death_causes.json", orient="split")
print(death_causes.head())

import requests
import pandas as pd
api_url = "https://api.yelp.com/v3/businesses/search"

# Set up parameter dictionary according to documentation
params = {"term": "bookstore", "location": "San Francisco"}
# Set up header dictionary w/ API key according to documentation
headers = {"Authorization": "Bearer {}".format(api_key)}
# Call the API
response = requests.get(api_url, params=params, headers=headers)

# Extract JSON data from the response
data = response.json()

# Load data to a data frame
cafes = pd.DataFrame(data['businesses'])

import pandas as pd
import requests
from pandas.io.json import json_normalize
# Set up headers, parameters, and API endpoint
api_url = "https://api.yelp.com/v3/businesses/search"
headers = {"Authorization": "Bearer {}".format(api_key)}
params = {"term": "bookstore", "location": "San Francisco"}

# Make the API call and extract the JSON data
response = requests.get(api_url, headers=headers, params=params)
data = response.json()

# Flatten data and load to data frame, with _ separators
bookstores = json_normalize(data["businesses"], sep="_")
>>> print(list(bookstores))

['alias', 'categories', 'coordinates_latitude', 'coordinates_longitude', ... 'location_address1', 'location_address2', 'location_address3', 'location_city', 'location_country', 'location_display_address', 'location_state', 'location_zip_code', ... 'url']

>>> print(bookstores.categories.head())
0 [{'alias': 'bookstores', 'title': 'Bookstores'}]
1 [{'alias': 'bookstores', 'title': 'Bookstores'...
2 [{'alias': 'bookstores', 'title': 'Bookstores'}]
3 [{'alias': 'bookstores', 'title': 'Bookstores'}]
4 [{'alias': 'bookstores', 'title': 'Bookstores'...
Name: categories, dtype: object

# Flatten categories data, bring in business details
df = json_normalize(data["businesses"],
                    sep="_", 
                    record_path="categories", # for nested data
                    meta=["name", "alias", "rating", ["coordinates","latitude"], 
                          													 ["coordinates","longitude"]
                         ], # list of attributes/columns
                    meta_prefix="biz_" # prefix to meta column names
                   )

# Put bookstore datasets together, renumber rows
bookstores = first_20_bookstores.append(next_20_bookstores, ignore_index=True)