def show(self):
pd = self.one_hot_code_for_categorical()
print pd.head()
self.multivariance(['SalePrice','OverallQual','GrLivArea','GarageCars'])
self.correlation('SalePrice')
self.categorical('SalePrice','OverallQual')
self.bivariance('SalePrice','GrLivArea')
self.missdata()
self.univariance("SalePrice")
def get_head(pd, lines=-1):
"""
get .head() of the DataFrame.
if 'lines' parameter is equel to -1 : use the deafult (usually 5),
otherwise : use it.
return type : DataFrame
"""
if lines == -1:
return pd.head()
else:
return pd.head(lines)
def get_only_head_and_tail(pd, lines=-1):
"""
get .head() + .tail() of the DataFrame.
if 'lines' parameter is equel to -1 : use the deafult (usually 5),
otherwise : use it.
return type : DataFrame
"""
if lines == -1:
return pd.head().append(pd.tail())
else:
lines = int(lines / 2)
return pd.head(lines).append(pd.tail(lines))
pass
def get_data(interval, symbols, time):
url = 'https://api.iextrading.com/1.0/stock/market/batch?symbols='
urlTypes = 'types=chart&'
#symbols = "AAPL,F,GE,SPY,FB&"
urlRange = 'range=1d&chartInterval=' + interval
# symbols = 'MMM,ABT&' # ,ABBV,ABMD,ACN,ATVI,ADBE,AMD,AAP,AES,AET,AMG,AFL,A,APD,AKAM,ALK,ALB,ARE,ALXN,ALGN,ALLE,AGN,ADS,LNT,ALL,GOOGL,GOOG,MO,AMZN,AEE,AAL,AEP,AXP,AIG,AMT,AWK,AMP,ABC,AME,AMGN,APH,APC,ADI,ANSS,ANTM,AON,AOS,APA,AIV,AAPL,AMAT,APTV,ADM,ARNC,ANET,AJG,AIZ,T,ADSK,ADP,AZO,AVB,AVY,BHGE,BLL,BAC,BK,BAX,BBT,BDX,BRK-B,BBY,BIIB,BLK,HRB,BA,BKNG,BWA,BXP,BSX,BHF,BMY,AVGO,BR,BF-B,CHRW,COG,CDNS,CPB,COF,CAH,KMX,CCL,CAT,CBOE,CBRE,CBS,CELG&'
r = DailyData.requests.get(url + symbols + urlTypes + urlRange)
json = r.json()
columns = [
'date', 'symbol', 'open', 'high', 'low', 'close', 'volume',
'change', 'changePercent', 'vwap'
]
pd = DailyData.pd.DataFrame(columns=columns)
for stock in json:
print(stock)
for chart in json[stock]['chart']:
if chart['minute'] == time:
pd = DailyData.map_to_chart(chart, pd, stock)
print(pd.head())
print(pd.tail())
pd.to_pickle("StockDataDaily/" + stock + ".pkl")
import numpy as np import scipy import matplotlib.pyplot as plt import seaborn as sns import pandas as pd # load file path = './data/2020_2019.csv' pd = pd.read_csv(path) print(pd.head())
names=["interactions_ID"])
hippo_all_sig_interactions = pd.read_table(
"C:\\Users\\libin\\UCSF\\eQTL\\hippocampus_lh_interactions",
sep="\t",
names=["chr", "start", "end", "score", "interactions_ID"])
hippo_self_single_end_sig = pd.merge(hippo_self_single_end_sig_ID,
hippo_all_sig_interactions,
on=["interactions_ID"],
how="inner")
sns.violinplot(hippo_self_single_end_sig).set(xlim=(3, 20))
sns.violinplot(hippo_self_single_end_sig["score"]).set(xlim=(3, 20))
print(hippo_self_single_end_sig["score"].mean())
print(hippo_self_single_end_sig["score"].median())
hippo_self_single_end_sig_score = pd.read_table(
"C:\\Users\\libin\\UCSF\\eQTL\\hippocampus_intersect_sig_single_end.score",
sep="\t",
names=["score"])
sns.violinplot(hippo_self_single_end_sig_score["score"]).set(xlim=(3, 20))
print(hippo_self_single_end_sig_score["score"].median())
print(hippo_self_single_end_sig_score["score"].mean())
hippocampus_sig_pairs = pd.read_table(
"C:\\Users\\libin\\UCSF\\eQTL\\Brain_Hippocampus.v7.signif_variant_gene_pairs.txt",
sep="\t")
sns.distplot(hippocampus_sig_pairs["score"])
sns.distplot(hippocampus_sig_pairs["tss_distance"])
print(hippocampus_sig_pairs["tss_distance"].max())
print(hippocampus_sig_pairs["tss_distance"].min())
pd.head(hippocampus_sig_pairs)
hippocampus_sig_pairs.head(n=2)
sns.boxplot(x="variable", y="value", data=hippo_plot1.melt(), palette="Set2")
#Using Panda library for Data extraction and Parsing it .
#https://pandas.pydata.org/docs/
import pandas as pd #import panda as pd
#Currently using data from yahoo
CSV_URL = 'https://query1.finance.yahoo.com/v7/finance/download/TSLA?period1=1558522033&period2=1590144433&interval=1d&events=history'
pd = pd.read_csv(CSV_URL) #Read the CSV
pd.head() #Print the top 10 lines jus to debug
pd.to_csv('TEST.csv') #Save it as TEST ?
#行添加 #df = df.append(df2) #行删除 # Drop rows with label 0 #df = df.drop(0) #基本功能 print pd.T print pd.axes print pd.dtype print pd.empty print pd.ndim print pd.shape print pd.size print pd.values print pd.head() print pd.tail() #描述性统计 #df.sum() #df.mean() #df.std() #count() #median() #mode() #min() #max() #abs() #prod #cumsum #cumprod
import pandas as pd
nbaReader = pd.read_csv('seasonstats.csv')
print(nbaReader)
nbaheadReader = pd.head()
nbainfoReader = pd.info()
imdbID = imdbID[-7:]
api_key = 'wh7z4wpjqbjm9v7u2xsfcu6f'
url = 'http://api.rottentomatoes.com/api/public/v1.0/movie_alias.json'
options = {'id': imdbID, 'type': 'imdb', 'apikey': api_key}
data = requests.get(url, params=options).text
data = json.loads(data)
movie_id = 0
movie_id = data['id']
print movie_id
if movie_id > 0:
url = 'http://api.rottentomatoes.com/api/public/v1.0/movies/%s/reviews.json' % movie_id
options = {'review_type':'top_critic', 'page_limit':20, 'page':1, 'apikey':api_key}
data = requests.get(url, params=options).text
data = json.loads(data)
frame = pd.DataFrame(data['reviews'])
frame.drop(['links', 'original_score'],inplace=True,axis=1)
frame.rename(columns={'freshness': 'fresh', 'date': 'review_date'}, inplace=True)
frame['imdb'] = imdbID
frame['rtid'] = movie_id
frame['title'] = movies.irow(row).title
return frame
#print frame.head()
else:
return None
rows = 30
dfs = [fetch_reviews(movies, r) for r in range(rows)]
pd = pd.concat(dfs, ignore_index=True)
print pd.head()
import pandas as pd
data = pd.read_csv("data.csv")
pd.head()
def dailypreps():
data = pd.read_csv('data/chargers/Allschwile_1.csv')
print(pd.head(data))
from sqlalchemy import create_engine
# set working directory
os.chdir('/Users/a.a.gonzalez.paje/Box Sync/Alberto/rapid_intel/3.0/code')
# create database and a table
data_base = sqlite3.connect("database.db")
# uncomment the following line in case you want to create a dummy table
#db.execute('create table person (firstname text, secondname text, age int)')
print os.getcwd()
# Import data (CSV format)
input_data = pd.read_csv("data/diamonds.csv")
# print file headers
input_data.head(3)
# Save dataframe into db
# Create your connection.
cnx = sqlite3.connect(':memory:')
# save data into db
sql.write_frame(input_data, name='diamonds', con=cnx)
# Get the dataframe back from the db
p2 = sql.read_frame('select * from diamonds', cnx)
pd.head(p2)
# TODO: multi word (ex.あきれる た
def get_sentidic_score(headword, *, type=None):
db = get_db(POLITELY_DICT_DB)
# TODO: type
res = db[SENTIDIC_COLLECTION_NAME].find_one({'headword': headword})
logger.info(res)
score = 0
if res:
score = res['score']
return score
if __name__ == "__main__":
pd = load_politly_dic(SENTIDIC_COLLECTION_NAME)
print(pd.shape, pd.head(10))
# db[NPTABLE_COLLECTION_NAME].update({'headword':'優れる', 'POS_jp':'動詞' },
# {'$set':{'headword':'優れる', 'reading': 'すぐれる', 'POS':'VERB', 'POS_jp':'動詞', 'eng':'be excellent'}},
# upsert=True)
senti_file_noun = app_home + '/dataset/posneg_dic/sent_nouns.dic'
# init_senti_dic(senti_file_noun, 'NOUN')
senti_file_verb = app_home + '/dataset/posneg_dic/sent_verb_adj.dic'
# get_sentidic_score('ない')
# get_sentidic_score('合う')
# get_sentidic_score('おいしい')
get_sentidic_score('無い')
get_sentidic_score('無駄')
# init_senti_dic(senti_file_verb, 'VERB')
def __init__(self, pd):
print(pd.head())
2、列和行必须通过数字引用，当您在列名和列的数字来回往复时，会引起混淆。
在接下来的几个任务中，我们将了解Pandas库，这是最流行的数据分析库之一。Pandas建立在NumPy上，但是更好地解决了NumPy的局限性。
'''
## 3. Read in a CSV file ##
'''
### 这节讲
'''
import pandas as pd
food_info = pd.read_csv('food_info.csv')
print(food_info, type(food_info))
## 4. Exploring the DataFrame ##
'''
### 这节讲 Pandas中最基础的方法和属性
例如：
pd.head()
pd.columns
pd.shape
dimensions = pd.shape
rows = dimensions[0]
columns = dimensions[1]
'''
print(food_info.head(3))
dimensions = food_info.shape
print(dimensions)
num_rows = dimensions[0]
print(num_rows)
