data-cleaning

问题 I've pulled some stock data from Quandl for both Crude Oil prices (WTI) and Caterpillar (CAT) price. When I concatenate the two dataframes together I'm left with some NaNs. My ultimate goal is to run a .Pearsonr() to assess the correlation (along with p-values), however I can't get Pearsonr() to work because of all the Nan's. So I'm trying to clean them up. When I use the .fillNA() function it doesn't seem to be working. I've even tried .interpolate() as well as .dropna(). None of them appear

问题 I am cleaning the monolingual corpus of Europarl for French (http://data.statmt.org/wmt19/translation-task/fr-de/monolingual/europarl-v7.fr.gz). The original raw data in .gz file (I downloaded using wget ). I want to extract the text and see how it looks like in order to further process the corpus. Using the following code to extract the text from gzip , I obtained data with the class being bytes . with gzip.open(file_path, 'rb') as f_in: print('type(f_in)=', type(f_in)) text = f_in.read()

问题 I am cleaning the monolingual corpus of Europarl for French (http://data.statmt.org/wmt19/translation-task/fr-de/monolingual/europarl-v7.fr.gz). The original raw data in .gz file (I downloaded using wget ). I want to extract the text and see how it looks like in order to further process the corpus. Using the following code to extract the text from gzip , I obtained data with the class being bytes . with gzip.open(file_path, 'rb') as f_in: print('type(f_in)=', type(f_in)) text = f_in.read()

问题 I am cleaning the monolingual corpus of Europarl for French (http://data.statmt.org/wmt19/translation-task/fr-de/monolingual/europarl-v7.fr.gz). The original raw data in .gz file (I downloaded using wget ). I want to extract the text and see how it looks like in order to further process the corpus. Using the following code to extract the text from gzip , I obtained data with the class being bytes . with gzip.open(file_path, 'rb') as f_in: print('type(f_in)=', type(f_in)) text = f_in.read()

问题 I have the following file data.json which I try to clean using Pyspark. {"positionmessage":{"callsign": "PPH1", "name": "testschip-10", "mmsi": 100,"timestamplast": "2019-08-01T00:00:08Z"}} {"positionmessage":{"callsign": "PPH2", "name": "testschip-11", "mmsi": 200,"timestamplast": "2019-08-01T00:00:01Z"}} {"positionmessage":{"callsign": "PPH3", "name": "testschip-10", "mmsi": 300,"timestamplast": "2019-08-01T00:00:05Z"}} {"positionmessage":{"callsign": , "name": , "mmsi": 200,"timestamplast"

问题 I have a data frame with a column of start dates and a column of end dates. I want to check the integrity of the dates by ensuring that the start date is before the end date (i.e. start_date < end_date).I have over 14,000 observations to run through. I have data in the form of: Start End 0 2008-10-01 2008-10-31 1 2006-07-01 2006-12-31 2 2000-05-01 2002-12-31 3 1971-08-01 1973-12-31 4 1969-01-01 1969-12-31 I have added a column to write the result to, even though I just want to highlight

问题 I have a data frame with a column of start dates and a column of end dates. I want to check the integrity of the dates by ensuring that the start date is before the end date (i.e. start_date < end_date).I have over 14,000 observations to run through. I have data in the form of: Start End 0 2008-10-01 2008-10-31 1 2006-07-01 2006-12-31 2 2000-05-01 2002-12-31 3 1971-08-01 1973-12-31 4 1969-01-01 1969-12-31 I have added a column to write the result to, even though I just want to highlight

问题 So I am trying to use Pandas to replace all NaN values in a table with the median across a particular range. I am working with a larger dataset but for example np.random.seed(0) rng = pd.date_range('2020-09-24', periods=20, freq='0.2H') df = pd.DataFrame({ 'Date': rng, 'Val': np.random.randn(len(rng)), 'Dist' :np.random.randn(len(rng)) }) df.Dist[df.Dist<=-0.6] = np.nan df.Val[df.Val<=-0.5] = np.nan What I want to do is replace the NaN values for Val and Dist with the median value for each

问题 I often come across the following common problem when cleaning the data there are some more common categories (let's say top 10 movie genres) and lots and lots of others which are sparse. Usual practice here would be to combine sparse genres into "Other" for example. Easily done when there are not many sparse categories: # Join bungalows as they are sparse classes into 1 df.property_type.replace(['Terraced bungalow','Detached bungalow', 'Semi-detached bungalow'], 'Bungalow', inplace=True) but

问题 I often come across the following common problem when cleaning the data there are some more common categories (let's say top 10 movie genres) and lots and lots of others which are sparse. Usual practice here would be to combine sparse genres into "Other" for example. Easily done when there are not many sparse categories: # Join bungalows as they are sparse classes into 1 df.property_type.replace(['Terraced bungalow','Detached bungalow', 'Semi-detached bungalow'], 'Bungalow', inplace=True) but