データの四隅を表示するメソッドを作成し、そのためにデータフレームにモンキー パッチを適用します。
def _sw(df, up_rows=10, down_rows=5, left_cols=4, right_cols=3, return_df=False):
''' display df data at four corners
A,B (up_pt)
C,D (down_pt)
parameters : up_rows=10, down_rows=5, left_cols=4, right_cols=3
usage:
df = pd.DataFrame(np.random.randn(20,10), columns=list('ABCDEFGHIJKLMN')[0:10])
df.sw(5,2,3,2)
df1 = df.set_index(['A','B'], drop=True, inplace=False)
df1.sw(5,2,3,2)
'''
#pd.set_printoptions(max_columns = 80, max_rows = 40)
ncol, nrow = len(df.columns), len(df)
# handle columns
if ncol <= (left_cols + right_cols) :
up_pt = df.ix[0:up_rows, :] # screen width can contain all columns
down_pt = df.ix[-down_rows:, :]
else: # screen width can not contain all columns
pt_a = df.ix[0:up_rows, 0:left_cols]
pt_b = df.ix[0:up_rows, -right_cols:]
pt_c = df[-down_rows:].ix[:,0:left_cols]
pt_d = df[-down_rows:].ix[:,-right_cols:]
up_pt = pt_a.join(pt_b, how='inner')
down_pt = pt_c.join(pt_d, how='inner')
up_pt.insert(left_cols, '..', '..')
down_pt.insert(left_cols, '..', '..')
overlap_qty = len(up_pt) + len(down_pt) - len(df)
down_pt = down_pt.drop(down_pt.index[range(overlap_qty)]) # remove overlap rows
dt_str_list = down_pt.to_string().split('\n') # transfer down_pt to string list
# Display up part data
print up_pt
start_row = (1 if df.index.names[0] is None else 2) # start from 1 if without index
# Display omit line if screen height is not enought to display all rows
if overlap_qty < 0:
print "." * len(dt_str_list[start_row])
# Display down part data row by row
for line in dt_str_list[start_row:]:
print line
# Display foot note
print "\n"
print "Index :",df.index.names
print "Column:",",".join(list(df.columns.values))
print "row: %d col: %d"%(len(df), len(df.columns))
print "\n"
return (df if return_df else None)
DataFrame.sw = _sw #add a method to DataFrame class
サンプルは次のとおりです。
>>> df = pd.DataFrame(np.random.randn(20,10), columns=list('ABCDEFGHIJKLMN')[0:10])
>>> df.sw()
A B C D .. H I J
0 -0.8166 0.0102 0.0215 -0.0307 .. -0.0820 1.2727 0.6395
1 1.0659 -1.0102 -1.3960 0.4700 .. 1.0999 1.1222 -1.2476
2 0.4347 1.5423 0.5710 -0.5439 .. 0.2491 -0.0725 2.0645
3 -1.5952 -1.4959 2.2697 -1.1004 .. -1.9614 0.6488 -0.6190
4 -1.4426 -0.8622 0.0942 -0.1977 .. -0.7802 -1.1774 1.9682
5 1.2526 -0.2694 0.4841 -0.7568 .. 0.2481 0.3608 -0.7342
6 0.2108 2.5181 1.3631 0.4375 .. -0.1266 1.0572 0.3654
7 -1.0617 -0.4743 -1.7399 -1.4123 .. -1.0398 -1.4703 -0.9466
8 -0.5682 -1.3323 -0.6992 1.7737 .. 0.6152 0.9269 2.1854
9 0.2361 0.4873 -1.1278 -0.2251 .. 1.4232 2.1212 2.9180
10 2.0034 0.5454 -2.6337 0.1556 .. 0.0016 -1.6128 -0.8093
..............................................................
15 1.4091 0.3540 -1.3498 -1.0490 .. 0.9328 0.3668 1.3948
16 0.4528 -0.3183 0.4308 -0.1818 .. 0.1295 1.2268 0.1365
17 -0.7093 1.3991 0.9501 2.1227 .. -1.5296 1.1908 0.0318
18 1.7101 0.5962 0.8948 1.5606 .. -0.6862 0.9558 -0.5514
19 1.0329 -1.2308 -0.6896 -0.5112 .. 0.2719 1.1478 -0.1459
Index : [None]
Column: A,B,C,D,E,F,G,H,I,J
row: 20 col: 10
>>> df.sw(4,2,3,4)
A B C .. G H I J
0 -0.8166 0.0102 0.0215 .. 0.3671 -0.0820 1.2727 0.6395
1 1.0659 -1.0102 -1.3960 .. 1.0984 1.0999 1.1222 -1.2476
2 0.4347 1.5423 0.5710 .. 1.6675 0.2491 -0.0725 2.0645
3 -1.5952 -1.4959 2.2697 .. 0.4856 -1.9614 0.6488 -0.6190
4 -1.4426 -0.8622 0.0942 .. -0.0947 -0.7802 -1.1774 1.9682
..............................................................
18 1.7101 0.5962 0.8948 .. -0.8592 -0.6862 0.9558 -0.5514
19 1.0329 -1.2308 -0.6896 .. -0.3954 0.2719 1.1478 -0.1459
Index : [None]
Column: A,B,C,D,E,F,G,H,I,J
row: 20 col: 10