pandas

问题 I have a Pandas DataFrame with a named index. I want to pass it off to a piece off code that takes a DataFrame, a column name, and some other stuff, and does a bunch of work involving that column. Only in this case the column I want to highlight is the index, but giving the index's label to this piece of code doesn't work because you can't extract an index like you can a regular column. For example, I can construct a DataFrame like this: import pandas as pd, numpy as np df=pd.DataFrame({'name

问题 I have the following DataFrame: P N ID Year Month TS 2016-06-26 19:30:00 263.600006 5.4 5 2016 6 2016-06-26 20:00:00 404.700012 5.6 5 2016 6 2016-06-26 21:10:00 438.600006 6.0 5 2016 6 2016-06-26 21:20:00 218.600006 5.6 5 2016 6 2016-07-02 16:10:00 285.300049 15.1 5 2016 7 I'm trying to add a new column based on the values of columns Year and Month something like the following def exp_records(row): return calendar.monthrange(row['Year'], row['Month'])[1] df['exp_counts'] = df.apply(exp

问题 when plotting Pandas dataframe using a histogram, sample dataframe data distance 0 5.680195 2 0.000000 3 7.974658 4 2.461387 5 9.703089 code I use to plot import matplotlib.pyplot as plt plt.hist(df['distance'].values) plt.show() I have this error "ValueError: range parameter must be finite." my attempt df['Round_Distance'] = df['distance'].round(1) 0 5.7 2 0.0 3 8.0 4 2.5 5 9.7 plot again, new error plt.hist(df['Round_Distance'].values) plt.show() ValueError: max must be larger than min in

问题 when plotting Pandas dataframe using a histogram, sample dataframe data distance 0 5.680195 2 0.000000 3 7.974658 4 2.461387 5 9.703089 code I use to plot import matplotlib.pyplot as plt plt.hist(df['distance'].values) plt.show() I have this error "ValueError: range parameter must be finite." my attempt df['Round_Distance'] = df['distance'].round(1) 0 5.7 2 0.0 3 8.0 4 2.5 5 9.7 plot again, new error plt.hist(df['Round_Distance'].values) plt.show() ValueError: max must be larger than min in

问题 So I have a pandas dataframe that looks like this: id_1 id_2 value1 value2 1 2 100 NAN 1 2 NAN 101 10 20 200 NAN 10 20 NAN 202 10 2 345 345 And I want a dataframe like this: id_1 id_2 value1 value2 1 2 100 101 10 20 200 202 a b c d Basically, if both ID columns match up, then there will definitely be a value-nan vs nan-value situation, and I want to combine the rows by just replacing the nans . Does pandas have a utility for this? It's not quite stacking, or melting. Maybe pivoting, but I'd

问题 Consider the following dataframes d1 and d1 d1 = pd.DataFrame([ [1, 2, 3], [2, 3, 4], [3, 4, 5], [1, 2, 3], [2, 3, 4], [3, 4, 5] ], columns=list('ABC')) d2 = pd.get_dummies(list('XYZZXY')) d1 A B C 0 1 2 3 1 2 3 4 2 3 4 5 3 1 2 3 4 2 3 4 5 3 4 5 d2 X Y Z 0 1 0 0 1 0 1 0 2 0 0 1 3 0 0 1 4 1 0 0 5 0 1 0 I need to get a new dataframe with a multi-index columns object that has the product of every combination of columns from d1 and d2 So far I've done this... from itertools import product pd

问题 I have a Pandas dataframe where I'd like to resample to every third Friday of the month. np.random.seed(0) #requested output: dates = pd.date_range("2018-01-01", "2018-08-31") dates_df = pd.DataFrame(data=np.random.random(len(dates)), index=dates) mask = (dates.weekday == 4) & (14 < dates.day) & (dates.day < 22) dates_df.loc[mask] But when a third Friday is missing (e.g. dropping Feb third Friday), I want to have the latest value (so as of 2018-02-15). Using the mask gives me the next value

问题 My final goal is making list which contain a pair for corresponding location of dataframes, like below [df_one_first_element, df_two_first_element, column_first, index_first] :[0.619159, 0.510162, 20140109,0.50], [0.264191,0.269053,20140213,0.50]... So I am trying to iterate two dataframe but got stuck now. How could I iterate two dataframe which has exactly same format but different data. For example, I have two dataframes; df_one and df_two that appear like the below: df_one = 20140109

问题 I have an array [0.2,0,0,0,0.3,0,0,0,0.4] . I'm using np.argsort to sort values and get that indexes. So, for my example, it will be something like [1,5,9,2,3,4,6...] . However, I would like to get array of indexes only for non zero values . In my example only [1,5,9] . How do I implement it in python with pandas and numpy ? 回答1: Using np.nonzero and indexing trick def sparse_argsort(arr): indices = np.nonzero(arr)[0] return indices[np.argsort(arr[indices])] sparse_argsort(a) array([0, 4, 8])

问题 I am having below pandas dataframe df . I am trying to rename the column names but it not working as expected. Code: mapping = {df.columns[0]:'Date', df.columns[1]: 'A', df.columns[2]:'B', df.columns[3]: 'C',df.columns[4]:'D', df.columns[5]: 'E',df.columns[6]:'F', df.columns[7]: 'G',df.columns[8]:'H', df.columns[9]: 'J'} df.rename(columns=mapping) Output of df.columns : MultiIndex(levels=[['A Index', 'B Index', 'C Index', 'D Index', 'E Index', 'F Index', 'G Index', 'H Index', 'I Index', 'J