10 Minutes to pandas

Concat

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

print(df)

# break it into pieces

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

print(pd.concat(pieces))

#           0         1         2         3

# 0  0.879526 -1.417311 -1.309299  0.287933

# 1 -1.194092  1.237536 -0.375177 -0.622846

# 2  1.449524  1.732103  1.866323  0.327194

# 3 -0.028595  1.047751  0.629286 -0.611354

# 4 -1.237406  0.878287  1.407587 -1.637072

# 5  0.536248  1.172208  0.405543  0.245162

# 6  0.166374  1.185840  0.132388 -0.832135

# 7  0.750722 -1.188307  1.306327  1.564907

# 8 -0.755132 -1.538270 -0.173119  1.341313

# 9 -0.572171  1.808220  0.688190 -0.672612

#           0         1         2         3

# 0  0.879526 -1.417311 -1.309299  0.287933

# 1 -1.194092  1.237536 -0.375177 -0.622846

# 2  1.449524  1.732103  1.866323  0.327194

# 3 -0.028595  1.047751  0.629286 -0.611354

# 4 -1.237406  0.878287  1.407587 -1.637072

# 5  0.536248  1.172208  0.405543  0.245162

# 6  0.166374  1.185840  0.132388 -0.832135

# 7  0.750722 -1.188307  1.306327  1.564907

# 8 -0.755132 -1.538270 -0.173119  1.341313

# 9 -0.572171  1.808220  0.688190 -0.672612

Join

类似 sql 里的 join （联表）

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

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

print(left)

print(right)

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

#    key  lval

# 0  foo     1

# 1  foo     2

#    key  rval

# 0  foo     4

# 1  foo     5

#    key  lval  rval

# 0  foo     1     4

# 1  foo     1     5

# 2  foo     2     4

# 3  foo     2     5

Merge

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

print(df)

s = df.iloc[3]

print(s)

df.append(s, ignore_index=True)

print(df)

print(df.append(s, ignore_index=True))

#           A         B         C         D

# 0 -1.744799 -0.745689 -0.066827 -0.993191

# 1  0.843984  0.902578  0.845040  1.336861

# 2  0.865214  1.151313  0.277192 -0.711557

# 3  0.917065 -0.948935  0.110977  0.047466

# 4 -1.309586  0.539592  1.956684 -0.117199

# 5 -0.431144  0.884499 -0.828626 -0.506894

# 6 -1.263993 -0.826366  1.426688 -0.434647

# 7 -0.567870 -0.086037  2.166162 -0.396294

# /

# A    0.917065

# B   -0.948935

# C    0.110977

# D    0.047466

# Name: 3, dtype: float64

# /

#           A         B         C         D

# 0 -1.744799 -0.745689 -0.066827 -0.993191

# 1  0.843984  0.902578  0.845040  1.336861

# 2  0.865214  1.151313  0.277192 -0.711557

# 3  0.917065 -0.948935  0.110977  0.047466

# 4 -1.309586  0.539592  1.956684 -0.117199

# 5 -0.431144  0.884499 -0.828626 -0.506894

# 6 -1.263993 -0.826366  1.426688 -0.434647

# 7 -0.567870 -0.086037  2.166162 -0.396294

# /

#           A         B         C         D

# 0  0.673341  0.211039  0.370737 -0.533311

# 1 -0.860026 -0.850189 -0.101193 -0.208695

# 2  1.684126  0.057633  0.775963  0.571528

# 3  0.340264 -1.576842  1.251407  1.703995

# 4  0.201961 -0.016234 -1.077373  0.477445

# 5 -0.096186 -0.766024  0.702740 -0.580853

# 6  0.941851  1.474317 -0.065384 -0.779173

# 7 -0.556754 -0.535569 -0.353260 -0.839585

# 8  0.340264 -1.576842  1.251407  1.703995

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