In [16]:
from ibmdbpy import IdaDataBase, IdaDataFrame
# @hidden_cell
# This connection object is used to access your data and contains your credentials.
# You might want to remove those credentials before you share your notebook.
idadb_5d05821b4dd8478b9b0a93ae9703695b = IdaDataBase(dsn='DASHDB;Database=BLUDB;Hostname=awh-yp-small02.services.dal.bluemix.net;Port=50000;PROTOCOL=TCPIP;UID=dash111495;PWD=#T9Yts@ctXM1')
df = IdaDataFrame(idadb_5d05821b4dd8478b9b0a93ae9703695b, 'DASH111495.IMAGE_DATA').as_dataframe()
df.head()
# You can close the database connection with the following code. Please keep the comment line with the @hidden_cell tag,
# because the close function displays parts of the credentials.
# @hidden_cell
# idadb_5d05821b4dd8478b9b0a93ae9703695b.close()
# To learn more about the ibmdby package, please read the documentation: http://pythonhosted.org/ibmdbpy/
Out[16]:
In [9]:
df.shape
Out[9]:
In [44]:
df_cars = df.loc[df['car'] == 'true']
df_cars.head()
Out[44]:
In [43]:
df_noncars = df.loc[df['car'] == 'false']
df_noncars.head()
Out[43]:
In [24]:
df_cars.describe()
Out[24]:
In [25]:
df_noncars.describe()
Out[25]:
In [41]:
df_cars.head().plot(legend=False)
Out[41]:
In [45]:
df_noncars.tail().plot(legend=False)
Out[45]:
In [51]:
df_cars_blue = df_cars.filter(regex='blue')
df_cars_blue.head().plot(legend=False)
df_cars_green = df_cars.filter(regex='green')
df_cars_green.head().plot(legend=False)
df_cars_red = df_cars.filter(regex='red')
df_cars_red.head().plot(legend=False)
Out[51]:
In [52]:
df_noncars_blue = df_noncars.filter(regex='blue')
df_noncars_blue.tail().plot(legend=False)
df_noncars_green = df_noncars.filter(regex='green')
df_noncars_green.tail().plot(legend=False)
df_noncars_red = df_noncars.filter(regex='red')
df_noncars_red.tail().plot(legend=False)
Out[52]:
In [64]:
import re
df_cars_green = df_cars_green.rename(columns=lambda x: re.sub('green','blue',x))
df_cars_rgb = df_cars_blue.add(df_cars_green,fill_value=0)
df_cars_red = df_cars_red.rename(columns=lambda x: re.sub('red','blue',x))
df_cars_rgb = df_cars_rgb.add(df_cars_red,fill_value=0)
df_cars_rgb = df_cars_rgb.rename(columns=lambda x: re.sub('blue','rgb',x))
df_cars_rgb.head()
Out[64]:
In [66]:
df_cars_rgb.head().plot(legend=False)
Out[66]:
In [68]:
df_noncars_green = df_noncars_green.rename(columns=lambda x: re.sub('green','blue',x))
df_noncars_rgb = df_noncars_blue.add(df_noncars_green,fill_value=0)
df_noncars_red = df_noncars_red.rename(columns=lambda x: re.sub('red','blue',x))
df_noncars_rgb = df_noncars_rgb.add(df_noncars_red,fill_value=0)
df_noncars_rgb = df_noncars_rgb.rename(columns=lambda x: re.sub('blue','rgb',x))
df_noncars_rgb.tail()
Out[68]:
In [71]:
df_noncars_rgb.tail().plot(legend=False)
In [155]:
df_cars_rgb_single = df_cars_rgb.iloc[0]
row0,row1,row2,row3,row4,row5,row6,row7,row8,row9 = ([] for x in range(10))
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 0'):
row0.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 1'):
row1.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 2'):
row2.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 3'):
row3.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 4'):
row4.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 5'):
row5.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 6'):
row6.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 7'):
row7.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 8'):
row8.append(value)
for key, value in df_cars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 9'):
row9.append(value)
df_cars_rgb_rows = pd.DataFrame(
{'row0': row0,
'row1': row1,
'row3': row3,
'row4': row4,
'row5': row5,
'row6': row6,
'row7': row7,
'row8': row8,
'row9': row9
})
df_cars_rgb_rows.plot(legend=False)
Out[155]:
In [147]:
df_noncars_rgb_single = df_noncars_rgb.iloc[0]
row0,row1,row2,row3,row4,row5,row6,row7,row8,row9 = ([] for x in range(10))
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 0'):
row0.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 1'):
row1.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 2'):
row2.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 3'):
row3.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 4'):
row4.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 5'):
row5.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 6'):
row6.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 7'):
row7.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 8'):
row8.append(value)
for key, value in df_noncars_rgb_single.iteritems(): # iter on both keys and values
if key.startswith('rgb row 9'):
row9.append(value)
df_noncars_rgb_rows = pd.DataFrame(
{'row0': row0,
'row1': row1,
'row3': row3,
'row4': row4,
'row5': row5,
'row6': row6,
'row7': row7,
'row8': row8,
'row9': row9
})
df_noncars_rgb_rows.plot(legend=False)
Out[147]:
In [150]:
df_noncars_rgb_rows.describe()
Out[150]:
In [154]:
df_cars_rgb_rows.describe()
Out[154]:
In [ ]: