pandas

问题 So below is the code I have been using. I'm a bit of a newb. I've been testing with just the head of the data because of the quota for using the API. Below is a snapshot of the dataframe: latitude longitude 0 -73.99107 40.730054 1 -74.000193 40.718803 2 -73.983849 40.761728 3 -73.97499915 40.68086214 4 -73.89488591 40.66471445 This is where I am getting tripped up. train['latlng'] = train.apply(lambda row: '{},{}'.format(row['latitude'], row['longitude']), axis=1) train['geocode_data'] =

问题 I am reading data from image files and I want to append this data into a single HDF file. Here is my code: datafile = pd.HDFStore(os.path.join(path,'imageData.h5')) for file in fileList: data = {'X Position' : pd.Series(xpos, index=index1), 'Y Position' : pd.Series(ypos, index=index1), 'Major Axis Length' : pd.Series(major, index=index1), 'Minor Axis Length' : pd.Series(minor, index=index1), 'X Velocity' : pd.Series(xVelocity, index=index1), 'Y Velocity' : pd.Series(yVelocity, index=index1) }

问题 df have id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 df want- add a new row to the measure for each id, called spend, calculated by subtracting measure=income - measure=savings, for each of the periods t1,t2,t3, for each id id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 1 spend 9 13 9 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 2 spend 1 14 68 Trying: df.loc[df['Measure'] == 'spend'] = df.loc[df[

问题 df have id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 df want- add a new row to the measure for each id, called spend, calculated by subtracting measure=income - measure=savings, for each of the periods t1,t2,t3, for each id id measure t1 t2 t3 1 savings 1 2 5 1 income 10 15 14 1 misc 5 5 5 1 spend 9 13 9 2 savings 3 6 12 2 income 4 20 80 2 misc 1 1 1 2 spend 1 14 68 Trying: df.loc[df['Measure'] == 'spend'] = df.loc[df[

问题 I'm trying to read a large and complex CSV file with pandas.read_csv. The exact command is pd.read_csv(filename, quotechar='"', low_memory=True, dtype=data_types, usecols= columns, true_values=['T'], false_values=['F']) I am pretty sure that the data types are correct. I can read the first 16 million lines (setting nrows=16000000) without problems but somewhere after this I get the following error ValueError: could not convert string to float: '1,123' As it seems, for some reason pandas

问题 So I have around 13 years of stock market data of daily low high open close. The problem is the markets are closed sometimes in between and hence Monday to Friday might not appear continuously sometimes. Look below Date Day Open High Low Close Adjusted Close 0 17-09-2007 Monday 6898 6977.2 6843 6897.1 6897.100098 1 18-09-2007 Tuesday 6921.15 7078.95 6883.6 7059.65 7059.649902 2 19-09-2007 Wednesday 7111 7419.35 7111 7401.85 7401.850098 3 20-09-2007 Thursday 7404.95 7462.9 7343.6 7390.15 7390

问题 So I have around 13 years of stock market data of daily low high open close. The problem is the markets are closed sometimes in between and hence Monday to Friday might not appear continuously sometimes. Look below Date Day Open High Low Close Adjusted Close 0 17-09-2007 Monday 6898 6977.2 6843 6897.1 6897.100098 1 18-09-2007 Tuesday 6921.15 7078.95 6883.6 7059.65 7059.649902 2 19-09-2007 Wednesday 7111 7419.35 7111 7401.85 7401.850098 3 20-09-2007 Thursday 7404.95 7462.9 7343.6 7390.15 7390

问题 I have the following sample CSV. ,cid1,cid2,cid3 rid1,0.1,0.4,0.3 rid2,1.0,0.1,0.5 rid3,0.2,0.5,0.1 rid4,0.3,0.4,0.8 rid5,0.2,0.3,0.7 rid6,0.9,0.2,0.1 rid7,0.4,0.8,0.9 rid8,0.6,0.5,0.7 rid9,0.3,0.9,0.4 I want to show n rows with the highest value for every column in the file. Ideally, I would like to get the following output (for n = 3). cid1 rid2 1.0 cid1 rid6 0.9 cid1 rid8 0.6 # Blank lines separating columns are optional. cid2 rid9 0.9 cid2 rid7 0.8 cid2 rid8 0.5 cid3 rid7 0.9 cid3 rid4 0

问题 I want to calculate the average for each hours using a CSV file: Below is my DATA SET: Timestamp Temperature 9/1/2016 0:00:08 53.8 9/1/2016 0:00:38 53.8 9/1/2016 0:01:08 53.8 9/1/2016 0:01:38 53.8 9/1/2016 0:02:08 53.8 9/1/2016 0:02:38 54.1 9/1/2016 0:03:08 54.1 9/1/2016 0:03:38 54.1 9/1/2016 0:04:38 54 9/1/2016 0:05:38 54 9/1/2016 0:06:08 54 9/1/2016 0:06:38 54 9/1/2016 0:07:08 54 9/1/2016 0:07:38 54 9/1/2016 0:08:08 54.1 9/1/2016 0:08:38 54.1 9/1/2016 0:09:38 54.1 9/1/2016 0:10:32 54 9/1

问题 I want to calculate the average for each hours using a CSV file: Below is my DATA SET: Timestamp Temperature 9/1/2016 0:00:08 53.8 9/1/2016 0:00:38 53.8 9/1/2016 0:01:08 53.8 9/1/2016 0:01:38 53.8 9/1/2016 0:02:08 53.8 9/1/2016 0:02:38 54.1 9/1/2016 0:03:08 54.1 9/1/2016 0:03:38 54.1 9/1/2016 0:04:38 54 9/1/2016 0:05:38 54 9/1/2016 0:06:08 54 9/1/2016 0:06:38 54 9/1/2016 0:07:08 54 9/1/2016 0:07:38 54 9/1/2016 0:08:08 54.1 9/1/2016 0:08:38 54.1 9/1/2016 0:09:38 54.1 9/1/2016 0:10:32 54 9/1