pandas

问题 I want to flatten the following JSON at each level and create a pandas dataframe per level, Im using flatten_json to do that but for that I need to loop through each level which creates multiple nested for loops: { "metadata": { "name": "abc", "time": "2020-04-01" }, "data": [ { "identifiers": [ { "type": "abc", "scheme": "def", "value": "123" }, { "type": "abc", "scheme": "def", "value": "123" } ], "name": "qwer", "type": "abd", "level1": [ { "identifiers": [ { "type": "abc", "scheme": "def"

问题 Can any one explain how to rectify this error Where do i get a detailed info of dask Can it replace pandas. How is it different from other dataframes, is it fast in processing Code: import dask.dataframe as dd df = dd.demo.make_timeseries('2000-01-01', '2000-12-31', freq='10s', partition_freq='1M',dtypes={'name': str, 'id': int, 'x': float, 'y': float}) print df o/p: Traceback (most recent call last): File "C:/Users/divya.nagandla/PycharmProjects/python/supressions1/dask.py", line 1, in

问题 Can any one explain how to rectify this error Where do i get a detailed info of dask Can it replace pandas. How is it different from other dataframes, is it fast in processing Code: import dask.dataframe as dd df = dd.demo.make_timeseries('2000-01-01', '2000-12-31', freq='10s', partition_freq='1M',dtypes={'name': str, 'id': int, 'x': float, 'y': float}) print df o/p: Traceback (most recent call last): File "C:/Users/divya.nagandla/PycharmProjects/python/supressions1/dask.py", line 1, in

问题 I've been getting the error: TypeError: float() argument must be a string or a number, not 'method'. Below is my snippet of code. I've checked other posts like this one: TypeError: float() argument must be a string or a number, not 'function' – Python/Sklearn but can't seem to get to the root cause of the error. Is python saying that my variables (y, x1, x2 etc.) are 'methods' which is why I'm receiving the error? If so, does anyone know how I can resolve this? Thanks in advance to anyone

问题 I want to sort a dataframe by all columns,and I find a way to solve that using df = df.apply( lambda x: x.sort_values()) and I used it to my data text1 = text text = text.apply( lambda x : x.sort_values()) text1 = text1.apply( lambda x : x.sort_values().values) text.head() text1.head() why not text = text.apply( lambda x : x.sort_values()) get a wrong answer,and what is the .vaules) function? text.head() Wave 2881.394531 2880.574219 2879.75293 2878.931641 2878.111328 N-1 0.220934 0.203666 0

问题 I want to sort a dataframe by all columns,and I find a way to solve that using df = df.apply( lambda x: x.sort_values()) and I used it to my data text1 = text text = text.apply( lambda x : x.sort_values()) text1 = text1.apply( lambda x : x.sort_values().values) text.head() text1.head() why not text = text.apply( lambda x : x.sort_values()) get a wrong answer,and what is the .vaules) function? text.head() Wave 2881.394531 2880.574219 2879.75293 2878.931641 2878.111328 N-1 0.220934 0.203666 0

问题 I am getting a weird behaviour from pandas, I want to resample my minute data to hourly data (using mean). My data looks as follows: Data.head() AAA BBB Time 2009-02-10 09:31:00 86.34 101.00 2009-02-10 09:36:00 86.57 100.50 2009-02-10 09:38:00 86.58 99.78 2009-02-10 09:40:00 86.63 99.75 2009-02-10 09:41:00 86.52 99.66 Data.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 961276 entries, 2009-02-10 09:31:00 to 2016-02-29 19:59:00 Data columns (total 2 columns): AAA 961276 non-null

问题 I am trying to apply a rolling function, with a 3 year window, on a pandas dataframe. import pandas as pd # Dummy data df = pd.DataFrame({'Product': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'Year': [2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018], 'IB': [2, 5, 8, 10, 7, 5, 10, 14], 'OB': [5, 8, 10, 12, 5, 10, 14, 20], 'Delta': [2, 2, 1, 3, -1, 3, 2, 4]}) # The function to be applied def get_ln_rate(ib, ob, delta): n_years = len(ib) return sum(delta)*np.log(ob[-1]/ib[0]) / (n_years * (ob[-1]

问题 I have a big dataset and when I try to run this code I get a memory error. user_by_movie = user_items.groupby(['user_id', 'movie_id'])['rating'].max().unstack() here is the error: ValueError: Unstacked DataFrame is too big, causing int32 overflow I have run it on another machine and it worked fine! how can I fix this error? 回答1: As it turns out this was not an issue on pandas 0.21. I am using a Jupyter notebook and I need the latest version of pandas for the rest of the code. So I did this:

问题 I have a big dataset and when I try to run this code I get a memory error. user_by_movie = user_items.groupby(['user_id', 'movie_id'])['rating'].max().unstack() here is the error: ValueError: Unstacked DataFrame is too big, causing int32 overflow I have run it on another machine and it worked fine! how can I fix this error? 回答1: As it turns out this was not an issue on pandas 0.21. I am using a Jupyter notebook and I need the latest version of pandas for the rest of the code. So I did this: