pandas快速入门

import numpy as np
import pandas as pd

s=pd.Series([1, 3, 5, np.nan, 6, 8])
s

dates=pd.date_range('20130101',periods=6)
dates

df=pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
df

df2=pd.DataFrame({'A': 1.,
                  'B': pd.Timestamp('20130102'), 
                  'C': pd.Series(1, index=list(range(4)), dtype='float32'),
                  'D': np.array([3] * 4, dtype='int32'), 
                  'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo'})
df2

df2.dtypes

df.describe()

df.sort_index(axis=1,ascending=False)

df.sort_values(by='B')

df['A']

df[0:3]

df['20130102':'20130104']

df.loc[:,['A','B']]

df.loc['20130102':'20130104', ['A', 'B']]

df.loc['20130102', ['A', 'B']]

df.loc[dates[0],'A']

df.at[dates[0], 'A']

df.iloc[3]

df.iloc[3:5,0:2]

df.iloc[[1,2,4],[0,2]]

df.iloc[1:3,:]

df.iloc[:,1:3]

df.iat[1,1]

df[df.A>0]

df[df>0]

df2=df.copy()
df2['E']=['one', 'one', 'two', 'three', 'four', 'three']
df2

df2[df2['E'].isin(['two','four'])]

sl=pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
sl

df2=df.copy()
df2[df2>0]=-df2
df2

df1=df.reindex(index=dates[0:4],columns=list(df.columns)+['E'])
df1.loc[dates[0]:dates[1], 'E'] = 1
df1

df1.dropna(how='any')

df1.fillna(value=5)

pd.isna(df1)

df.mean()

df.mean(1)

s=pd.Series([1, 3, 5, np.nan, 6, 8],index=dates).shift(2)
s

df.sub(s,axis='index')

df.apply(np.cumsum)

df.apply(lambda x:x.max()-x.min())

s=pd.Series(np.random.randint(0,7,size=10))
s

s=pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s.str.lower()

df=pd.DataFrame(np.random.randn(10,4))
df

pieces=[df[:3],df[3:7],df[7:]]
pd.concat(pieces)

left=pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})
left

right=pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})
right

pd.merge(left,right,on='key')

left=pd.DataFrame({'key':['foo','bar'],'lval':[1,2]})
left

right=pd.DataFrame({'key':['foo','bar'],'rval':[4,5]})
right

pd.merge(left,right,on='key')

df=pd.DataFrame(np.random.randn(8,4),columns=['A','B','C','D'])
df

s=df.iloc[3]
df.append(s,ignore_index=True)

df=pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                       'foo', 'bar', 'foo', 'foo'],
                 'B': ['one', 'one', 'two', 'three',
                       'two', 'two', 'one', 'three'],
                 'C': np.random.randn(8),
                 'D': np.random.randn(8)})
df

df.groupby('A').sum()

tuples=list(zip(*[['bar', 'bar', 'baz', 'baz',
                    'foo', 'foo', 'qux', 'qux'],
                   ['one', 'two', 'one', 'two',
                    'one', 'two', 'one', 'two']]))
tuples

index=pd.MultiIndex.from_tuples(tuples,names=['first','second'])
index

df=pd.DataFrame(np.random.randn(8,2),index=index,columns=['A','B'])
df2 = df[:4]
df2

stacked=df2.stack()
stacked

stacked.unstack()

stacked.unstack(1)

stacked.unstack(0)

df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
                   'B': ['A', 'B', 'C'] * 4,
                   'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
                   'D': np.random.randn(12),
                   'E': np.random.randn(12)})
df

pd.pivot_table(df,values='D',index=['A','B'],columns=['C'])

rng=pd.date_range('1/1/2012',periods=100,freq='S')
ts=pd.Series(np.random.randint(0,500,len(rng)),index=rng)
ts.resample('5Min').sum()

rng=pd.date_range('3/6/2012 00:00',periods=5,freq='D')
ts=pd.Series(np.random.randn(len(rng)),rng)
ts

ts_utc=ts.tz_localize('UTC')
ts_utc

ts_utc.tz_convert('US/Eastern')

rng=pd.date_range('1/1/2012',periods=5,freq='M')
ts=pd.Series(np.random.randn(len(rng)),index=rng)
ts

ps=ts.to_period()
ps

ps.to_timestamp()

prng=pd.period_range('1990Q1','2000Q4',freq='Q-NOV')
ts=pd.Series(np.random.randn(len(prng)),prng)
ts.index=(prng.asfreq('M','e')++1).asfreq('H','s')++9
ts.head()

df=pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
                 "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
df['grade']=df['raw_grade'].astype('category')
df['grade']

df['grade'].cat.categories=["very good","good","very bad"]
df['grade']=df['grade'].cat.set_categories(["very bad", "bad", "medium","good", "very good"])
df['grade']

df.sort_values(by='grade')

df.groupby('grade').size()

import matplotlib.pyplot as plt
ts=pd.Series(np.random.randn(1000),
              index=pd.date_range('1/1/2000', periods=1000))
ts=ts.cumsum()
ts.plot()

df=pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=['A','B','C','D'])
df=df.cumsum()
plt.figure()
df.plot()
plt.legend(loc='best')

df.to_csv('foo.csv')

pd.read_csv('foo.csv')

df.to_hdf('foo.h5','df')

pd.read_hdf('foo.h5','df')

df.to_excel('foo.xlsx',sheet_name='sheet1')

pd.read_excel('foo.xlsx','sheet1',index_col=None,na_values=['NA'])

if pd.Series([False, True, False]):
    print('True')