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2016-05-12 18:09
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pandas:powerful Python data analysis tookit
——Wes McKinney & PyData Development Team，Release 0.18.0，March 17, 2016Customarily,we import as follows:
import numpy as np import pandas as pd import matplotlib.pyplot as plt
1. Object Creation
1.1 Series
Series is a one-dimensional labeled array capable of holding any data type(integers,strings,floating point numbers,Python objects,etc.).The axis labels are collectively referred to as the index(轴标签统称为索引).The basic method to create a Series is to call:>>> s = pd.Series(data,index = index)
Here,data can be many different things:
a Python dict
an ndarray
a scalar value
The passed index is a list of axis labels.Thus,this separates into a few cases depending on what data is:
From ndarray
If data is an ndarray,index must be the same length as data.If no index is passed,one will be created havig values [0,1,3,…,len(data)-1].
>>> s = pd.Series(np.random.randn(5),index = ['a','b','c','d','e']) >>> s a -0.159223 b 2.317106 c -0.341460 d -1.499552 e 0.400351 dtype: float64 >>> s.index Index([u'a', u'b', u'c', u'd', u'e'], dtype='object') >>> pd.Series(np.random.randn(5)) 0 0.785536 1 -1.014011 2 -0.120812 3 0.289870 4 0.705393 dtype: float64
From dict
If data is a dict,if index is passed the values in data corresponding to the labels in the index will be pulled out.Otherwise,an index will be constructed from the sorted keys of the dict,if possible.
>>> d = {'a':0.,'b':1.,'c':2.}
>>> pd.Series(d)
a 0
b 1
c 2
dtype: float64
>>> pd.Series(d,index = list('bcda'))
b 1
c 2
d NaN
a 0
dtype: float64**Note:**NaN(not a number) is the standard missing data marker used in pandas.
From scalar value
If data is a scalar value,an index must be provided.The value will be repeated to match the length of index.
>>> pd.Series(5.,index = ['a','b','c','d','e']) a 5 b 5 c 5 d 5 e 5 dtype: float64
1.1.1 Series is ndarray-like
Series acts very similarly to a ndarray,and is a valid argument to most numpy functions.However,things like slicing also slice the index.>>> s[0] -0.15922308848832653 >>> s[:3] a -0.159223 b 2.317106 c -0.341460 dtype: float64 >>> s[s > s.median()] e 0.400351 b 2.317106 dtype: float64 >>> s[[4,3,2]] b 2.317106 e 0.400351 a -0.159223 dtype: float64 >>> np.exp(s) d 0.223230 c 0.710732 a 0.852806 e 1.492348 b 10.146272 dtype: float64
1.1.2 Series is dict-like
>>> s['a'] -0.15922308848832653 >>> s['e'] = 12 >>> s d -1.499552 c -0.341460 a -0.159223 e 12.000000 b 2.317106 dtype: float64 >>> 'e' in s True >>> 'f' in s False >>> s['f'] KeyError: 'f'
Using the get method,a missing label will return None or specified default:
>>> s.get('f')
>>> s.get('f',np.nan)
nan
>>> s.get('e',np.nan)
12.01.1.3 Vectorized operations and label alignment with Series
When doing data analysis,as with raw numpy arrays looping through Series value-by-value is usually not necessary.Series can be also be passed into most numpy methods expecting an ndarray.>>> s+s d -2.999105 c -0.682919 a -0.318446 e 24.000000 b 4.634213 dtype: float64 >>> s * 2 >>> np.exp(s)
A key different between Series and ndarray is that operations betwee Series automatically align the data based on label.Thus,you can write computations without giving consideration to whether the Series involved have the same labels.
>>> s[1:] + s[:-1] a -0.318446 b NaN c -0.682919 d NaN e 24.000000 dtype: float64
Creating a Series by passing a list of values,letting pandas create a default integer index:
>>> s = pd.Series([1,2,3,np.nan,6,8]) 0 1 1 3 2 5 3 NaN 4 6 5 8 dtype: float64
1.2 DataFrame
Creating a DataFrame by passing a numpy array,with a datetime index and labeled columns:>>> dates = pd.date_range('20130101',periods=6)
>>> dates
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-01, ..., 2013-01-06]
Length: 6, Freq: D, Timezone: None
>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
>>> df
A B C D
2013-01-01 0.716330 -1.782610 0.809990 0.319876
2013-01-02 -0.171806 -0.526268 0.206743 -1.246213
2013-01-03 -1.774970 1.890517 -0.773496 -0.930083
2013-01-04 0.537348 -0.870212 -1.227291 1.322823
2013-01-05 -0.897589 1.275171 1.064439 -2.021186
2013-01-06 0.130427 -1.067145 -1.273118 1.786337
[6 rows x 4 columns]Creating a DataFrame by passing a dict of objects that can be converted to series-like:
>>> df2 = pd.DataFrame({'A':1.,
'B':pd.Timestamp('20130102'),
'C':pd.Series(1,index=range(4),dtype='float32'),
'D':np.array([3]*4,dtype='int32'),
'E':pd.Categorical(['test','train','test','train']),
'F':'foo'})
>>>df2
A B C D E F
0 1 2013-01-02 1 3 test foo
1 1 2013-01-02 1 3 train foo
2 1 2013-01-02 1 3 test foo
3 1 2013-01-02 1 3 train foo
[4 rows x 6 columns]Having specific dtypes
>>> df2.dtypes A float64 B datetime64[ns] C float32 D int32 E object F object dtype: object
If you’re using IPython,tab completion for column names (as well as public attributes) is automatically enabled.
>>> df2.<TAB> Display all 210 possibilities? (y or n) df2.A df2.from_csv df2.rank df2.B df2.from_dict df2.rdiv df2.C df2.from_items df2.reindex df2.D df2.from_records df2.reindex_axis df2.E df2.ftypes df2.reindex_like df2.F df2.ge df2.rename df2.T df2.get df2.rename_axis df2.abs df2.get_dtype_counts df2.reorder_levels df2.add df2.get_ftype_counts df2.replace df2.add_prefix df2.get_value df2.resample df2.add_suffix df2.get_values df2.reset_index df2.align df2.groupby df2.rfloordiv df2.all df2.gt df2.rmod df2.any df2.head df2.rmul df2.append df2.hist df2.rpow df2.apply df2.iat df2.rsub df2.applymap df2.icol df2.rtruediv df2.as_blocks df2.idxmax df2.save df2.as_matrix df2.idxmin df2.select df2.asfreq df2.iget_value df2.set_index df2.astype df2.iloc df2.set_value df2.at df2.index df2.shape df2.at_time df2.info df2.shift df2.axes df2.insert df2.skew df2.between_time df2.interpolate df2.sort df2.bfill df2.irow df2.sort_index df2.blocks df2.is_copy df2.sortlevel df2.bool df2.isin df2.squeeze df2.boxplot df2.isnull df2.stack df2.clip df2.iteritems df2.std df2.clip_lower df2.iterkv df2.sub df2.clip_upper df2.iterrows df2.subtract df2.columns df2.itertuples df2.sum df2.combine df2.ix 4000 df2.swapaxes df2.combineAdd df2.join df2.swaplevel df2.combineMult df2.keys df2.tail df2.combine_first df2.kurt df2.take df2.compound df2.kurtosis df2.to_clipboard df2.consolidate df2.last df2.to_csv df2.convert_objects df2.last_valid_index df2.to_dense df2.copy df2.le df2.to_dict df2.corr df2.load df2.to_excel df2.corrwith df2.loc df2.to_gbq df2.count df2.lookup df2.to_hdf df2.cov df2.lt df2.to_html df2.cummax df2.mad df2.to_json df2.cummin df2.mask df2.to_latex df2.cumprod df2.max df2.to_msgpack df2.cumsum df2.mean df2.to_panel df2.delevel df2.median df2.to_period df2.describe df2.merge df2.to_pickle df2.diff df2.min df2.to_records df2.div df2.mod df2.to_sparse df2.divide df2.mode df2.to_sql df2.dot df2.mul df2.to_stata df2.drop df2.multiply df2.to_string df2.drop_duplicates df2.ndim df2.to_timestamp df2.dropna df2.ne df2.to_wide df2.dtypes df2.notnull df2.transpose df2.duplicated df2.pct_change df2.truediv df2.empty df2.pivot df2.truncate df2.eq df2.pivot_table df2.tshift df2.equals df2.plot df2.tz_convert df2.eval df2.pop df2.tz_localize df2.ffill df2.pow df2.unstack df2.fillna df2.prod df2.update df2.filter df2.product df2.values df2.first df2.quantile df2.var df2.first_valid_index df2.query df2.where df2.floordiv df2.radd df2.xs >>> df2.
2. Viewing Data
See the top&bottom rows of the frame>>> df.head() >>> df.tail() >>> df.head(10) >>> df.tail(10)
Display the index,columns,and the underlying numpy data
>>> df.index <class 'pandas.tseries.index.DatetimeIndex'> [2013-01-01, ..., 2013-01-06] Length: 6, Freq: D, Timezone: None >>> df.columns Index([u'A', u'B', u'C', u'D'], dtype='object') >>> df.values array([[ 0.71632974, -1.78261015, 0.80999048, 0.31987599], [-0.17180562, -0.52626809, 0.20674317, -1.24621339], [-1.77496978, 1.89051681, -0.77349583, -0.93008323], [ 0.53734751, -0.87021202, -1.22729091, 1.32282329], [-0.89758898, 1.27517093, 1.06443943, -2.02118609], [ 0.13042695, -1.06714528, -1.27311829, 1.78633711]])
Describe shows a quick statistic summary of your data
>>> df.describe() A B C D count 6.000000 6.000000 6.000000 6.000000 mean -0.243377 -0.180091 -0.198789 -0.128074 std 0.943312 1.439183 1.031516 1.513146 min -1.774970 -1.782610 -1.273118 -2.021186 25% -0.716143 -1.017912 -1.113842 -1.167181 50% -0.020689 -0.698240 -0.283376 -0.305104 75% 0.435617 0.824811 0.659179 1.072086 max 0.716330 1.890517 1.064439 1.786337 [8 rows x 4 columns]
Transposing your data
>>> df.T 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.716330 -0.171806 -1.774970 0.537348 -0.897589 0.130427 B -1.782610 -0.526268 1.890517 -0.870212 1.275171 -1.067145 C 0.809990 0.206743 -0.773496 -1.227291 1.064439 -1.273118 D 0.319876 -1.246213 -0.930083 1.322823 -2.021186 1.786337 [4 rows x 6 columns]
sorting by an axis(sort_index method)
>>> df.sort_index(axis=1,ascending=False) D C B A 2013-01-01 0.319876 0.809990 -1.782610 0.716330 2013-01-02 -1.246213 0.206743 -0.526268 -0.171806 2013-01-03 -0.930083 -0.773496 1.890517 -1.774970 2013-01-04 1.322823 -1.227291 -0.870212 0.537348 2013-01-05 -2.021186 1.064439 1.275171 -0.897589 2013-01-06 1.786337 -1.273118 -1.067145 0.130427 [6 rows x 4 columns] >>> df.sort_index(axis=0,ascending=False) A B C D 2013-01-06 0.130427 -1.067145 -1.273118 1.786337 2013-01-05 -0.897589 1.275171 1.064439 -2.021186 2013-01-04 0.537348 -0.870212 -1.227291 1.322823 2013-01-03 -1.774970 1.890517 -0.773496 -0.930083 2013-01-02 -0.171806 -0.526268 0.206743 -1.246213 2013-01-01 0.716330 -1.782610 0.809990 0.319876 [6 rows x 4 columns] >>> df.sort_index(axis=1,ascending=False).sort_index(axis=0,ascending=False) D C B A 2013-01-06 1.786337 -1.273118 -1.067145 0.130427 2013-01-05 -2.021186 1.064439 1.275171 -0.897589 2013-01-04 1.322823 -1.227291 -0.870212 0.537348 2013-01-03 -0.930083 -0.773496 1.890517 -1.774970 2013-01-02 -1.246213 0.206743 -0.526268 -0.171806 2013-01-01 0.319876 0.809990 -1.782610 0.716330 [6 rows x 4 columns]
Sorting by values(sort method,0:descending,1:ascengding)
>>> df.sort(['A','B'],ascending=[0,1])
3. Selection
3.1 Getting
Selecting a single column,which yields a Series,equivalent to df.A>>> df.A 2013-01-01 0.716330 2013-01-02 -0.171806 2013-01-03 -1.774970 2013-01-04 0.537348 2013-01-05 -0.897589 2013-01-06 0.130427 Freq: D, Name: A, dtype: float64 >>> df['A'] 2013-01-01 0.716330 2013-01-02 -0.171806 2013-01-03 -1.774970 2013-01-04 0.537348 2013-01-05 -0.897589 2013-01-06 0.130427 Freq: D, Name: A, dtype: float64
Selecting via [],which slices the rows.
>>> df[0:3] A B C D 2013-01-01 0.716330 -1.782610 0.809990 0.319876 2013-01-02 -0.171806 -0.526268 0.206743 -1.246213 2013-01-03 -1.774970 1.890517 -0.773496 -0.930083 >>>df['20130101':'20130103'] A B C D 2013-01-01 0.716330 -1.782610 0.809990 0.319876 2013-01-02 -0.171806 -0.526268 0.206743 -1.246213 2013-01-03 -1.774970 1.890517 -0.773496 -0.930083 [3 rows x 4 columns]
3.2 Selecting by Label
For getting a cross section(横截面) using a label>>> df.loc[dates[0]] A 0.716330 B -1.782610 C 0.809990 D 0.319876 Name: 2013-01-01 00:00:00, dtype: float64
Selecting on a multi-axis by label
>>> df.loc[:,['A','B']] A B 2013-01-01 0.716330 -1.782610 2013-01-02 -0.171806 -0.526268 2013-01-03 -1.774970 1.890517 2013-01-04 0.537348 -0.870212 2013-01-05 -0.897589 1.275171 2013-01-06 0.130427 -1.067145 [6 rows x 2 columns]
Showing label slicing,both endpoints are included
>>> df.loc['20130102':'20130104',['A','B']] A B 2013-01-02 -0.171806 -0.526268 2013-01-03 -1.774970 1.890517 2013-01-04 0.537348 -0.870212 [3 rows x 2 columns]
Reduction in the dimensions of the returned object
>>> df.loc['20130102',['A','B']] A -0.171806 B -0.526268 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value(标量)
>>> df.loc[dates[0],'A'] 0.71632974391895454
For getting fast access to a scalar(equiv to the prior method)
>>> df.at[dates[0],'A'] 0.71632974391895454
3.3 Selection by Position
Select via the position of the passed integers>>> df.iloc[3] #第四行 A 0.537348 B -0.870212 C -1.227291 D 1.322823 Name: 2013-01-04 00:00:00, dtype: float64
By integer slices,acting similar to numpy/python
>>> df.iloc[3:5,0:2] A B 2013-01-04 0.537348 -0.870212 2013-01-05 -0.897589 1.275171 [2 rows x 2 columns]
By lists of integer position locations,similar to the numpy/python style
>>> df.iloc[[1,2,4],[0,2]] A C 2013-01-02 -0.171806 0.206743 2013-01-03 -1.774970 -0.773496 2013-01-05 -0.897589 1.064439 [3 rows x 2 columns]
For slicing rows explicitly
>>> df.iloc[1:3,:] A B C D 2013-01-02 -0.171806 -0.526268 0.206743 -1.246213 2013-01-03 -1.774970 1.890517 -0.773496 -0.930083 [2 rows x 4 columns]
For slicing columns explicitly
>>> df.iloc[:,1:3] B C 2013-01-01 -1.782610 0.809990 2013-01-02 -0.526268 0.206743 2013-01-03 1.890517 -0.773496 2013-01-04 -0.870212 -1.227291 2013-01-05 1.275171 1.064439 2013-01-06 -1.067145 -1.273118 [6 rows x 2 columns]
For getting a value explicitly
>>> df.iloc[1,1] -0.52626808513391488
For getting fast access to a scalar(equiv to the prior method)
>>> df.iat[1,1] -0.52626808513391488
3.4 Boolean Indexing
Using a single column’s values to select data>>> df[df.A>0] >>> df[df['A']>0] A B C D 2013-01-02 0.859761 0.755971 1.371420 0.271600 2013-01-03 0.606392 0.077458 0.251290 2.134013 2013-01-05 0.022155 -0.216343 -1.179598 0.431374 2013-01-06 2.676268 2.295133 -2.132639 0.702915 [4 rows x 4 columns]
A where operation for getting.
>>> df[df>0] A B C D 2013-01-01 NaN NaN NaN 0.209321 2013-01-02 0.859761 0.755971 1.371420 0.271600 2013-01-03 0.606392 0.077458 0.251290 2.134013 2013-01-04 NaN NaN 0.946518 NaN 2013-01-05 0.022155 NaN NaN 0.431374 2013-01-06 2.676268 2.295133 NaN 0.702915 [6 rows x 4 columns]
Using the isin() method for filtering(过滤):
>>> df[df>0] >>> df2 = df.copy() >>> df2['E'] = ['one','one','two','three','four','three'] >>> df2 A B C D E 2013-01-01 -0.234954 -1.346601 -1.030691 0.209321 one 2013-01-02 0.859761 0.755971 1.371420 0.271600 one 2013-01-03 0.606392 0.077458 0.251290 2.134013 two 2013-01-04 -0.938926 -0.749240 0.946518 -0.248072 three 2013-01-05 0.022155 -0.216343 -1.179598 0.431374 four 2013-01-06 2.676268 2.295133 -2.132639 0.702915 three [6 rows x 5 columns] >>> df2[df2.E.isin(['two','four'])] A B C D E 2013-01-03 0.606392 0.077458 0.251290 2.134013 two 2013-01-05 0.022155 -0.216343 -1.179598 0.431374 four [2 rows x 5 columns]
3.5 Setting
Setting a new column automatically aligns the data by the indexes>>> s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
>>> s1
2013-01-02 1
2013-01-03 2
2013-01-04 3
2013-01-05 4
2013-01-06 5
2013-01-07 6
Freq: D, dtype: int64
>>> df['F'] = s1Setting values by label
>>> df.at[dates[0],'A'] = 0
Setting values by position
>>> df.iat[0,1] = 0
Setting by assigning with a numpy array
>>> df.loc[:,'D'] = np.array([5*len(df)])
The result of the prior setting operations
df
A B C D F
2013-01-01 0.000000 0.000000 -1.030691 30 NaN
2013-01-02 0.859761 0.755971 1.371420 30 1
2013-01-03 0.606392 0.077458 0.251290 30 2
2013-01-04 -0.938926 -0.749240 0.946518 30 3
2013-01-05 0.022155 -0.216343 -1.179598 30 4
2013-01-06 2.676268 2.295133 -2.132639 30 5
[6 rows x 5 columns]
A where operation with setting
>>> df2 = df.copy()
>>> df2[df2>0] = -df2
A B C D F
2013-01-01 0.000000 0.000000 -1.030691 -30 NaN
2013-01-02 -0.859761 -0.755971 -1.371420 -30 -1
2013-01-03 -0.606392 -0.077458 -0.251290 -30 -2
2013-01-04 -0.938926 -0.749240 -0.946518 -30 -3
2013-01-05 -0.022155 -0.216343 -1.179598 -30 -4
2013-01-06 -2.676268 -2.295133 -2.132639 -30 -5
[6 rows x 5 columns]
4. Missing Data
pandas primarily use the value np.nan to represent missing data.It is default not included in computations.Reindexing allows you to change/add/delete the index on a specified axis.This returns a copy of the data.
>>> df1 = df.reindex(index = dates[0:4],columns = list(df.columns) + ['E'])
>>> df1.loc[dates[0]:dates[1],'E'] = 1
>>> df
A B C D F
2013-01-01 0.000000 0.000000 -1.030691 30 NaN
2013-01-02 0.859761 0.755971 1.371420 30 1
2013-01-03 0.606392 0.077458 0.251290 30 2
2013-01-04 -0.938926 -0.749240 0.946518 30 3
2013-01-05 0.022155 -0.216343 -1.179598 30 4
2013-01-06 2.676268 2.295133 -2.132639 30 5
[6 rows x 5 columns]
>>> df1
A B C D F E
2013-01-01 0.000000 0.000000 -1.030691 30 NaN 1
2013-01-02 0.859761 0.755971 1.371420 30 1 1
2013-01-03 0.606392 0.077458 0.251290 30 2 NaN
2013-01-04 -0.938926 -0.749240 0.946518 30 3 NaN
[4 rows x 6 columns]
To drop any rows that have missing data.
>>> df1.dropna(how='any') A B C D F E 2013-01-02 0.859761 0.755971 1.37142 30 1 1 [1 rows x 6 columns] >>> df1.fillna(value=5) A B C D F E 2013-01-01 0.000000 0.000000 -1.030691 30 5 1 2013-01-02 0.859761 0.755971 1.371420 30 1 1 2013-01-03 0.606392 0.077458 0.251290 30 2 5 2013-01-04 -0.938926 -0.749240 0.946518 30 3 5 [4 rows x 6 columns]
To get the boolean mask where values are nan
>>> pd.isnull(df1)/df1.isnull() A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True [4 rows x 6 columns]
5. Operations
5.1 Stats
Operations in general exclude missing data.Performing a descriptive statistic
>>> df.mean() A 0.537608 B 0.360497 C -0.295617 D 30.000000 F 3.000000 dtype: float64
Same operation on the other axis
>>> df.mean(1) 2013-01-01 7.242327 2013-01-02 6.797430 2013-01-03 6.587028 2013-01-04 6.451670 2013-01-05 6.525243 2013-01-06 7.567752 Freq: D, dtype: float64
Operating with objects that have different dimensionality and need aligment.In addition,pandas automatically broadcasts along the specified dimension.
>>> s = pd.Series([1,3,5,np.nan,6,8], index
ac1b
=dates).shift(2)
>>> s
2013-01-01 NaN
2013-01-02 NaN
2013-01-03 1
2013-01-04 3
2013-01-05 5
2013-01-06 NaN
Freq: D, dtype: float64
>>> df.sub(s,axis = 'index')
A B C D F
2013-01-01 NaN NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN NaN
2013-01-03 -0.393608 -0.922542 -0.748710 29 1
2013-01-04 -3.938926 -3.749240 -2.053482 27 0
2013-01-05 -4.977845 -5.216343 -6.179598 25 -1
2013-01-06 NaN NaN NaN NaN NaN
[6 rows x 5 columns]
5.2 Apply
Applying function to the data>>> df.apply(np.cumsum) #累计求和
A B C D F
2013-01-01 0.000000 0.000000 -1.030691 30 NaN
2013-01-02 0.859761 0.755971 0.340729 60 1
2013-01-03 1.466153 0.833429 0.592019 90 3
2013-01-04 0.527227 0.084189 1.538537 120 6
2013-01-05 0.549382 -0.132154 0.358939 150 10
2013-01-06 3.225651 2.162979 -1.773700 180 15
[6 rows x 5 columns]
>>> df.apply(lambda x:x.max() - x.min())
A 3.615194
B 3.044374
C 3.504059
D 0.000000
F 4.000000
dtype: float64
5.3 Histogramming
>>> s = pd.Series(np.random.randint(0,7,size=10)) >>> s 0 6 1 3 2 6 3 0 4 0 5 6 6 4 7 3 8 0 9 1 dtype: int64 >>> s.value_counts() 6 3 0 3 3 2 4 1 1 1 dtype: int64
5.4 String Methods
Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions by default (and in some cases always uses them)>>> s = pd.Series(['A','B','C','AaBa','Baca',np.nan,'CABA','dog','cat']) >>> s.str.lower() 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
6.Merge
6.1 Concat
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