intro-pandas


Introduction to Pandas

Forked from 10 Minutes to pandas


In [1]:
%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Object Creation

http://pandas.pydata.org/pandas-docs/stable/10min.html#object-creation

Creating a Series by passing a list of values, letting pandas create a default integer index:


In [2]:
s = pd.Series([1,3,5,np.nan,6,8])
s


Out[2]:
0     1
1     3
2     5
3   NaN
4     6
5     8
dtype: float64

Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns:


In [3]:
dates = pd.date_range('20130101', periods=6)
dates


Out[3]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

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


Out[4]:
A B C D
2013-01-01 1.451161 1.083850 -0.229906 1.513570
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496
2013-01-04 -0.259608 -0.756092 0.048437 0.632222
2013-01-05 1.181925 1.542669 -0.249990 -0.452952
2013-01-06 -0.384743 -0.320193 0.010833 -0.348848

Creating a DataFrame by passing a dict of objects that can be converted to series-like.


In [5]:
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


Out[5]:
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

In [6]:
df2.dtypes


Out[6]:
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:

In [13]: df2.<TAB>
df2.A                  df2.boxplot
df2.abs                df2.C
df2.add                df2.clip
df2.add_prefix         df2.clip_lower
df2.add_suffix         df2.clip_upper
df2.align              df2.columns
df2.all                df2.combine
df2.any                df2.combineAdd
df2.append             df2.combine_first
df2.apply              df2.combineMult
df2.applymap           df2.compound
df2.as_blocks          df2.consolidate
df2.asfreq             df2.convert_objects
df2.as_matrix          df2.copy
df2.astype             df2.corr
df2.at                 df2.corrwith
df2.at_time            df2.count
df2.axes               df2.cov
df2.B                  df2.cummax
df2.between_time       df2.cummin
df2.bfill              df2.cumprod
df2.blocks             df2.cumsum
df2.bool               df2.D

As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity.


In [7]:
df.head()


Out[7]:
A B C D
2013-01-01 1.451161 1.083850 -0.229906 1.513570
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496
2013-01-04 -0.259608 -0.756092 0.048437 0.632222
2013-01-05 1.181925 1.542669 -0.249990 -0.452952

In [8]:
df.tail(3)


Out[8]:
A B C D
2013-01-04 -0.259608 -0.756092 0.048437 0.632222
2013-01-05 1.181925 1.542669 -0.249990 -0.452952
2013-01-06 -0.384743 -0.320193 0.010833 -0.348848

In [9]:
df.index


Out[9]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

In [10]:
df.columns


Out[10]:
Index(['A', 'B', 'C', 'D'], dtype='object')

In [11]:
df.values


Out[11]:
array([[ 1.45116055,  1.08385004, -0.22990568,  1.51357   ],
       [ 0.08186229, -0.69472223,  0.98943061, -0.6274273 ],
       [ 0.07014546,  0.6677724 , -1.96319787, -0.9214962 ],
       [-0.25960773, -0.7560921 ,  0.04843707,  0.63222203],
       [ 1.18192468,  1.54266925, -0.24999032, -0.45295249],
       [-0.38474322, -0.32019307,  0.01083273, -0.34884813]])

In [12]:
df.describe()


Out[12]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.356790 0.253881 -0.232399 -0.034155
std 0.770203 0.976789 0.961965 0.922310
min -0.384743 -0.756092 -1.963198 -0.921496
25% -0.177169 -0.601090 -0.244969 -0.583809
50% 0.076004 0.173790 -0.109536 -0.400900
75% 0.906909 0.979831 0.039036 0.386954
max 1.451161 1.542669 0.989431 1.513570

Transposing your data


In [13]:
df.T


Out[13]:
2013-01-01 00:00:00 2013-01-02 00:00:00 2013-01-03 00:00:00 2013-01-04 00:00:00 2013-01-05 00:00:00 2013-01-06 00:00:00
A 1.451161 0.081862 0.070145 -0.259608 1.181925 -0.384743
B 1.083850 -0.694722 0.667772 -0.756092 1.542669 -0.320193
C -0.229906 0.989431 -1.963198 0.048437 -0.249990 0.010833
D 1.513570 -0.627427 -0.921496 0.632222 -0.452952 -0.348848

Sorting by an axis


In [14]:
df.sort_index(axis=1, ascending=False)


Out[14]:
D C B A
2013-01-01 1.513570 -0.229906 1.083850 1.451161
2013-01-02 -0.627427 0.989431 -0.694722 0.081862
2013-01-03 -0.921496 -1.963198 0.667772 0.070145
2013-01-04 0.632222 0.048437 -0.756092 -0.259608
2013-01-05 -0.452952 -0.249990 1.542669 1.181925
2013-01-06 -0.348848 0.010833 -0.320193 -0.384743

Sorting by values


In [15]:
df.sort_values(by='B')


Out[15]:
A B C D
2013-01-04 -0.259608 -0.756092 0.048437 0.632222
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-06 -0.384743 -0.320193 0.010833 -0.348848
2013-01-03 0.070145 0.667772 -1.963198 -0.921496
2013-01-01 1.451161 1.083850 -0.229906 1.513570
2013-01-05 1.181925 1.542669 -0.249990 -0.452952

While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix.

Getting

Selecting a single column, which yields a Series, equivalent to df.A


In [16]:
df['A']


Out[16]:
2013-01-01    1.451161
2013-01-02    0.081862
2013-01-03    0.070145
2013-01-04   -0.259608
2013-01-05    1.181925
2013-01-06   -0.384743
Freq: D, Name: A, dtype: float64

In [17]:
df[0:3]


Out[17]:
A B C D
2013-01-01 1.451161 1.083850 -0.229906 1.513570
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496

In [18]:
df['20130102':'20130104']


Out[18]:
A B C D
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496
2013-01-04 -0.259608 -0.756092 0.048437 0.632222

Selection by Label

For getting a cross section using a label

SKIPPING FOR NOW. http://pandas.pydata.org/pandas-docs/stable/10min.html#selection-by-label

Selection by Position

Select via the position of the passed integers


In [19]:
df.iloc[3]


Out[19]:
A   -0.259608
B   -0.756092
C    0.048437
D    0.632222
Name: 2013-01-04 00:00:00, dtype: float64

By integer slices, acting similar to numpy/python


In [20]:
df.iloc[3:5,0:2]


Out[20]:
A B
2013-01-04 -0.259608 -0.756092
2013-01-05 1.181925 1.542669

By lists of integer position locations, similar to the numpy/python style


In [21]:
df.iloc[[1,2,4],[0,2]]


Out[21]:
A C
2013-01-02 0.081862 0.989431
2013-01-03 0.070145 -1.963198
2013-01-05 1.181925 -0.249990

For slicing rows explicitly


In [22]:
df.iloc[1:3,:]


Out[22]:
A B C D
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496

For slicing columns explicitly


In [23]:
df.iloc[:,1:3]


Out[23]:
B C
2013-01-01 1.083850 -0.229906
2013-01-02 -0.694722 0.989431
2013-01-03 0.667772 -1.963198
2013-01-04 -0.756092 0.048437
2013-01-05 1.542669 -0.249990
2013-01-06 -0.320193 0.010833

For getting a value explicitly


In [24]:
df.iloc[1,1]


Out[24]:
-0.69472222701715824

For getting fast access to a scalar (equiv to the prior method)


In [25]:
df.iat[1,1]


Out[25]:
-0.69472222701715824

Boolean Indexing

Using a single column’s values to select data.


In [26]:
df[df.A > 0]


Out[26]:
A B C D
2013-01-01 1.451161 1.083850 -0.229906 1.513570
2013-01-02 0.081862 -0.694722 0.989431 -0.627427
2013-01-03 0.070145 0.667772 -1.963198 -0.921496
2013-01-05 1.181925 1.542669 -0.249990 -0.452952

A where operation for getting.


In [27]:
df > 0


Out[27]:
A B C D
2013-01-01 True True False True
2013-01-02 True False True False
2013-01-03 True True False False
2013-01-04 False False True True
2013-01-05 True True False False
2013-01-06 False False True False

In [28]:
df[df > 0]


Out[28]:
A B C D
2013-01-01 1.451161 1.083850 NaN 1.513570
2013-01-02 0.081862 NaN 0.989431 NaN
2013-01-03 0.070145 0.667772 NaN NaN
2013-01-04 NaN NaN 0.048437 0.632222
2013-01-05 1.181925 1.542669 NaN NaN
2013-01-06 NaN NaN 0.010833 NaN

Using the isin() method for filtering:


In [29]:
df2 = df.copy()
df2['E'] = ['one', 'one','two','three','four','three']
df2


Out[29]:
A B C D E
2013-01-01 1.451161 1.083850 -0.229906 1.513570 one
2013-01-02 0.081862 -0.694722 0.989431 -0.627427 one
2013-01-03 0.070145 0.667772 -1.963198 -0.921496 two
2013-01-04 -0.259608 -0.756092 0.048437 0.632222 three
2013-01-05 1.181925 1.542669 -0.249990 -0.452952 four
2013-01-06 -0.384743 -0.320193 0.010833 -0.348848 three

In [30]:
df2[df2['E'].isin(['two','four'])]


Out[30]:
A B C D E
2013-01-03 0.070145 0.667772 -1.963198 -0.921496 two
2013-01-05 1.181925 1.542669 -0.249990 -0.452952 four

Setting

Setting a new column automatically aligns the data by the indexes


In [31]:
s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
s1


Out[31]:
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

In [32]:
df['F'] = s1

Setting values by label


In [33]:
df.at[dates[0],'A'] = 0

Setting values by position


In [34]:
df.iat[0,1] = 0

Setting by assigning with a numpy array


In [35]:
df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations


In [36]:
df


Out[36]:
A B C D F
2013-01-01 0.000000 0.000000 -0.229906 5 NaN
2013-01-02 0.081862 -0.694722 0.989431 5 1
2013-01-03 0.070145 0.667772 -1.963198 5 2
2013-01-04 -0.259608 -0.756092 0.048437 5 3
2013-01-05 1.181925 1.542669 -0.249990 5 4
2013-01-06 -0.384743 -0.320193 0.010833 5 5

A where operation with setting.


In [37]:
df2 = df.copy()
df2[df2 > 0] = -df2
df2


Out[37]:
A B C D F
2013-01-01 0.000000 0.000000 -0.229906 -5 NaN
2013-01-02 -0.081862 -0.694722 -0.989431 -5 -1
2013-01-03 -0.070145 -0.667772 -1.963198 -5 -2
2013-01-04 -0.259608 -0.756092 -0.048437 -5 -3
2013-01-05 -1.181925 -1.542669 -0.249990 -5 -4
2013-01-06 -0.384743 -0.320193 -0.010833 -5 -5

Missing Data

http://pandas.pydata.org/pandas-docs/stable/10min.html#missing-data

pandas primarily uses the value np.nan to represent missing data. It is by 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.


In [38]:
df


Out[38]:
A B C D F
2013-01-01 0.000000 0.000000 -0.229906 5 NaN
2013-01-02 0.081862 -0.694722 0.989431 5 1
2013-01-03 0.070145 0.667772 -1.963198 5 2
2013-01-04 -0.259608 -0.756092 0.048437 5 3
2013-01-05 1.181925 1.542669 -0.249990 5 4
2013-01-06 -0.384743 -0.320193 0.010833 5 5

In [39]:
df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1.loc[dates[0]:dates[1],'E'] = 1
df1


Out[39]:
A B C D F E
2013-01-01 0.000000 0.000000 -0.229906 5 NaN 1
2013-01-02 0.081862 -0.694722 0.989431 5 1 1
2013-01-03 0.070145 0.667772 -1.963198 5 2 NaN
2013-01-04 -0.259608 -0.756092 0.048437 5 3 NaN

To drop any rows that have missing data.


In [40]:
df1.dropna(how='any')


Out[40]:
A B C D F E
2013-01-02 0.081862 -0.694722 0.989431 5 1 1

Filling missing data


In [41]:
df1.fillna(value=5)


Out[41]:
A B C D F E
2013-01-01 0.000000 0.000000 -0.229906 5 5 1
2013-01-02 0.081862 -0.694722 0.989431 5 1 1
2013-01-03 0.070145 0.667772 -1.963198 5 2 5
2013-01-04 -0.259608 -0.756092 0.048437 5 3 5

To get the boolean mask where values are nan


In [42]:
pd.isnull(df1)


Out[42]:
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

Stats

Operations in general exclude missing data.

Performing a descriptive statistic


In [43]:
df.mean()


Out[43]:
A    0.114930
B    0.073239
C   -0.232399
D    5.000000
F    3.000000
dtype: float64

Same operation on the other axis


In [44]:
df.mean(axis=1)


Out[44]:
2013-01-01    1.192524
2013-01-02    1.275314
2013-01-03    1.154944
2013-01-04    1.406547
2013-01-05    2.294921
2013-01-06    1.861179
Freq: D, dtype: float64

Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension.


In [45]:
s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
s


Out[45]:
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

In [46]:
df.sub(s, axis='index')


Out[46]:
A B C D F
2013-01-01 NaN NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN NaN
2013-01-03 -0.929855 -0.332228 -2.963198 4 1
2013-01-04 -3.259608 -3.756092 -2.951563 2 0
2013-01-05 -3.818075 -3.457331 -5.249990 0 -1
2013-01-06 NaN NaN NaN NaN NaN

Apply

Applying functions to the data


In [47]:
df.apply(np.cumsum)


Out[47]:
A B C D F
2013-01-01 0.000000 0.000000 -0.229906 5 NaN
2013-01-02 0.081862 -0.694722 0.759525 10 1
2013-01-03 0.152008 -0.026950 -1.203673 15 3
2013-01-04 -0.107600 -0.783042 -1.155236 20 6
2013-01-05 1.074325 0.759627 -1.405226 25 10
2013-01-06 0.689581 0.439434 -1.394393 30 15

In [48]:
df.apply(lambda x: x.max() - x.min())


Out[48]:
A    1.566668
B    2.298761
C    2.952628
D    0.000000
F    4.000000
dtype: float64

Histogramming


In [49]:
s = pd.Series(np.random.randint(0, 7, size=10))
s


Out[49]:
0    0
1    4
2    2
3    3
4    5
5    1
6    2
7    4
8    0
9    2
dtype: int64

In [50]:
s.value_counts()


Out[50]:
2    3
4    2
0    2
5    1
3    1
1    1
dtype: int64

String Methods


In [51]:
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [52]:
s.str.lower()


Out[52]:
0       a
1       b
2       c
3    aaba
4    baca
5     NaN
6    caba
7     dog
8     cat
dtype: object

Concat

pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.

Concatenating pandas objects together with concat():


In [57]:
df = pd.DataFrame(np.random.randn(10, 4))
df


Out[57]:
0 1 2 3
0 -0.607808 0.017562 0.419972 -1.662403
1 -0.379599 0.497103 -1.151861 -0.767817
2 -0.949214 -0.244919 1.549104 1.368718
3 -0.091203 -0.134465 -0.575829 0.079611
4 0.766208 0.506876 -1.297151 -0.387270
5 -0.419647 -0.448643 -0.390209 -0.537567
6 1.080452 -1.028649 -0.375224 0.639619
7 -0.559779 -0.308850 -1.431094 -2.015278
8 0.723019 -1.597068 0.540808 0.495331
9 -1.046595 0.829075 0.644139 0.874297

In [58]:
pieces = [df[:3], df[3:7], df[7:]]
pieces


Out[58]:
[          0         1         2         3
 0 -0.607808  0.017562  0.419972 -1.662403
 1 -0.379599  0.497103 -1.151861 -0.767817
 2 -0.949214 -0.244919  1.549104  1.368718,
           0         1         2         3
 3 -0.091203 -0.134465 -0.575829  0.079611
 4  0.766208  0.506876 -1.297151 -0.387270
 5 -0.419647 -0.448643 -0.390209 -0.537567
 6  1.080452 -1.028649 -0.375224  0.639619,
           0         1         2         3
 7 -0.559779 -0.308850 -1.431094 -2.015278
 8  0.723019 -1.597068  0.540808  0.495331
 9 -1.046595  0.829075  0.644139  0.874297]

In [59]:
pd.concat(pieces)


Out[59]:
0 1 2 3
0 -0.607808 0.017562 0.419972 -1.662403
1 -0.379599 0.497103 -1.151861 -0.767817
2 -0.949214 -0.244919 1.549104 1.368718
3 -0.091203 -0.134465 -0.575829 0.079611
4 0.766208 0.506876 -1.297151 -0.387270
5 -0.419647 -0.448643 -0.390209 -0.537567
6 1.080452 -1.028649 -0.375224 0.639619
7 -0.559779 -0.308850 -1.431094 -2.015278
8 0.723019 -1.597068 0.540808 0.495331
9 -1.046595 0.829075 0.644139 0.874297

Join

SQL style merges.


In [61]:
left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
left


Out[61]:
key lval
0 foo 1
1 foo 2

In [62]:
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
right


Out[62]:
key rval
0 foo 4
1 foo 5

In [63]:
pd.merge(left, right, on='key')


Out[63]:
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5

Append

Append rows to a dataframe.


In [65]:
df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
df


Out[65]:
A B C D
0 1.105242 -1.036707 1.032622 0.870278
1 0.723815 -0.188268 -1.465932 -0.213963
2 2.201238 -0.204618 0.099377 -0.087141
3 -0.462891 -1.972687 1.509800 1.231281
4 1.245002 -1.562285 -0.941729 -0.361184
5 -3.372217 0.068841 1.667736 -0.577858
6 -0.425616 0.397228 1.185438 -0.749950
7 -0.257397 -1.069318 -0.625966 1.991807

In [67]:
s = df.iloc[3]
s


Out[67]:
A   -0.462891
B   -1.972687
C    1.509800
D    1.231281
Name: 3, dtype: float64

In [68]:
df.append(s, ignore_index=True)


Out[68]:
A B C D
0 1.105242 -1.036707 1.032622 0.870278
1 0.723815 -0.188268 -1.465932 -0.213963
2 2.201238 -0.204618 0.099377 -0.087141
3 -0.462891 -1.972687 1.509800 1.231281
4 1.245002 -1.562285 -0.941729 -0.361184
5 -3.372217 0.068841 1.667736 -0.577858
6 -0.425616 0.397228 1.185438 -0.749950
7 -0.257397 -1.069318 -0.625966 1.991807
8 -0.462891 -1.972687 1.509800 1.231281

Grouping

http://pandas.pydata.org/pandas-docs/stable/10min.html#grouping

By “group by” we are referring to a process involving one or more of the following steps

  • Splitting the data into groups based on some criteria
  • Applying a function to each group independently
  • Combining the results into a data structure

In [69]:
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


Out[69]:
A B C D
0 foo one -1.512222 -1.596827
1 bar one 0.107643 0.266170
2 foo two -0.792270 0.447479
3 bar three 0.555783 -0.042216
4 foo two -0.889059 -0.857961
5 bar two 0.632459 -0.858737
6 foo one -0.558497 0.031724
7 foo three -0.339345 1.007338

Grouping and then applying a function sum to the resulting groups.


In [71]:
df.groupby('A').sum()


Out[71]:
C D
A
bar 1.295884 -0.634784
foo -4.091392 -0.968246

Grouping by multiple columns forms a hierarchical index, which we then apply the function.


In [72]:
df.groupby(['A','B']).sum()


Out[72]:
C D
A B
bar one 0.107643 0.266170
three 0.555783 -0.042216
two 0.632459 -0.858737
foo one -2.070719 -1.565102
three -0.339345 1.007338
two -1.681329 -0.410482

In [53]:
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()


Out[53]:
<matplotlib.axes._subplots.AxesSubplot at 0x1126b2a90>

On DataFrame, plot() is a convenience to plot all of the columns with labels:


In [54]:
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')


Out[54]:
<matplotlib.legend.Legend at 0x114ccbd30>
<matplotlib.figure.Figure at 0x114ce3470>

CSV


In [55]:
df.to_csv('foo.csv')

In [56]:
pd.read_csv('foo.csv').head()


Out[56]:
Unnamed: 0 A B C D
0 2000-01-01 1.608151 -1.437353 -0.961114 -0.056711
1 2000-01-02 -0.647573 -1.544272 0.113719 -0.451111
2 2000-01-03 -0.925200 -2.002480 -0.161309 -0.063620
3 2000-01-04 -1.508885 -2.153251 -0.809877 -2.167732
4 2000-01-05 -0.107604 -1.473513 -1.619227 -0.887293

HDF5

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Excel

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