W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook.pdf - W7 Data Wrangling Join Combine and Reshape Jupyter Notebook Join Combine and

# W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook.pdf

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10/26/2019 W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook localhost:8888/notebooks/W7 Data Wrangling - Join%2C Combine and Reshape.ipynb 1/44 Join, Combine and Reshape Data may be spread across number of files or databases Combine, join and rearrange data is an important skill 1. Hierarchical Indexing Multiple index levels on an axis. Higher dimensional data in lower dimensional form. When looking at a Series or DataFrame with multi-index, you will see "gaps" in the higher index, which means "same as the one above". MultiIndex table example In [1]: In [2]: Out[2]: a 1 100 2 101 3 102 b 1 103 3 104 c 1 105 2 106 d 2 107 3 108 dtype: int32 import warnings warnings.simplefilter(action = 'ignore' , category = FutureWarning) # disable Fu import pandas as pd import numpy as np # 2d data in 1d form data = pd.Series(np.arange( 100 , 109 ), index = [[ 'a' , 'a' , 'a' , 'b' , 'b' , 'c' , 'c' , 'd' , 'd' ], [ 1 , 2 , 3 , 1 , 3 , 1 , 2 , 2 , 3 ]]) #same length so that th data
10/26/2019 W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook localhost:8888/notebooks/W7 Data Wrangling - Join%2C Combine and Reshape.ipynb 2/44 In [3]: 'partial indexing' enables us to concisely select subsets of data. Selection is also possible for "inner" level of indexes. In [4]: In [5]: In [6]: In [7]: Hierarchical indexing has important role in reshaping data and group=based operations. eg - forming pivot table. You could rearrange data into a DataFrame using its 'unstack' method. The inverse operation of stack is 'stack'. Out[3]: MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3]], labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1, 1, 2]]) Out[4]: 1 103 3 104 dtype: int32 Out[5]: b 1 103 3 104 c 1 105 2 106 dtype: int32 Out[6]: b 1 103 3 104 d 2 107 3 108 dtype: int32 Out[7]: a 102 b 104 d 108 dtype: int32 data.index #levels are the unique set, labels are the index of levels data[ 'b' ] # select a subgroup data[ 'b' : 'c' ] #select two subgroups data.loc[[ 'b' , 'd' ]] #select a list of subgroups data.loc[:, 3 ] #for each subgroup, choose the element at index 3
10/26/2019 W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook localhost:8888/notebooks/W7 Data Wrangling - Join%2C Combine and Reshape.ipynb 3/44 In [8]: In [9]: In [10]: In a DataFrame, either axis can have hierarchical index. The hierarchical indexes can have names and they will be shown in the console output. NOTE - Be careful not to mix-up index names with row labels. With partial column indexing, we can select groups of columns. A 'MultiIndex' can be created by itself and then reused. Out[8]: a 1 100 2 101 3 102 b 1 103 3 104 c 1 105 2 106 d 2 107 3 108 dtype: int32 Out[9]: 1 2 3 a 100.0 101.0 102.0 b 103.0 NaN 104.0 c 105.0 106.0 NaN d NaN 107.0 108.0 Out[10]: a 1 100.0 2 101.0 3 102.0 b 1 103.0 3 104.0 c 1 105.0 2 106.0 d 2 107.0 3 108.0 dtype: float64 data data.unstack() # re-arrange 1d form into 2d form by the two indices data.unstack().stack() #switch between 1d and 2d form, i.e., series vs data
10/26/2019 W7 Data Wrangling - Join, Combine and Reshape - Jupyter Notebook localhost:8888/notebooks/W7 Data Wrangling - Join%2C Combine and Reshape.ipynb 4/44 In [11]: In [12]: In [13]: Out[11]: Ohio Colorado Green Red Green a 1 0 1 2 2 3 4 5 b 1 6 7 8 2 9 10 11 Out[12]: state Ohio Colorado color Green Red Green key1 key2 a 1 0 1 2 2 3 4 5 b 1 6 7 8 2 9 10 11 Out[13]: color Green Red key1 key2 a 1 0 1 2 3 4 b 1 6 7 2 9 10 frame = pd.DataFrame(np.arange( 12 ).reshape(( 4 , 3 )), index = [[

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