pandas

问题 I met problem with processing of spark wide dataframe (about 9000 columns and sometimes more). Task: Create wide DF via groupBy and pivot. Transform columns to vector and processing in to KMeans from pyspark.ml. So I made extensive frame and try to create vector with VectorAssembler, cached it and trained on it KMeans. It took about 11 minutes for assembling and 2 minutes for KMeans for 7 different count of clusters on my pc in standalone mode for frame ~500x9000. Another side this processing

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive. I need to join B to A under the condition that a given element a of A.datetime corresponds to B[B['datetime'] <= a]]['datetime'].max() There are a couple ways to do this, but I would like the most efficient way. Option 1 Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive. I need to join B to A under the condition that a given element a of A.datetime corresponds to B[B['datetime'] <= a]]['datetime'].max() There are a couple ways to do this, but I would like the most efficient way. Option 1 Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that

问题 Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive. I need to join B to A under the condition that a given element a of A.datetime corresponds to B[B['datetime'] <= a]]['datetime'].max() There are a couple ways to do this, but I would like the most efficient way. Option 1 Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that

问题 Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive. I need to join B to A under the condition that a given element a of A.datetime corresponds to B[B['datetime'] <= a]]['datetime'].max() There are a couple ways to do this, but I would like the most efficient way. Option 1 Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and