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