pandas

问题 I'm learning Pandas and Numpy, currently going through this section of the tutorial. I'm new to Python altogether, so this is probably a basic beginner's question. Given this data frame: df = pd.DataFrame(np.random.randn(4, 3), columns=['A', 'B', 'C'], index=pd.date_range('1/1/2000', periods=4)) df.iloc[3:7] = np.nan I can't explain the difference between the following results of df.agg: Call 1: df.agg(sum) #Result: A NaN B NaN C NaN dtype: float64 Call 2: df.agg('sum') #Result: A -1.776752 B

问题 I created a pandas dataframe sample and it tried to sum for every 3 rows: import pandas as pd import numpy as np d={'A':[100,110,120,175,164,169,155,153,156,200]} df=pd.DataFrame(d) A 0 100 1 110 2 120 3 175 4 164 5 169 6 155 7 153 8 156 9 200 0 NaN 1 NaN 2 330.0 #this is the result tho 3 405.0 4 459.0 5 508.0 6 488.0 7 477.0 8 464.0 9 509.0 Name: sum, dtype: float64 And i want to display the equation process like this: NaN NaN 330.0 = 100+110+120 405.0 = 110+120+175 459.0 . 508.0 . 488.0 .

问题 I have a large dataframe with text that I want to use to find matches from a list of words (around 1k words in there). I have managed to get the absence/presence of the word from the list in the dataframe, but it is also important to me to know which word matched. Sometimes there is exact match with more than one word from the list, I would like to have them all. I tried to use the code below, but it gives me partial matches - syllables instead of full words. #this is a code to recreate the

问题 I have a large dataframe with text that I want to use to find matches from a list of words (around 1k words in there). I have managed to get the absence/presence of the word from the list in the dataframe, but it is also important to me to know which word matched. Sometimes there is exact match with more than one word from the list, I would like to have them all. I tried to use the code below, but it gives me partial matches - syllables instead of full words. #this is a code to recreate the

问题 I have a large dataframe with text that I want to use to find matches from a list of words (around 1k words in there). I have managed to get the absence/presence of the word from the list in the dataframe, but it is also important to me to know which word matched. Sometimes there is exact match with more than one word from the list, I would like to have them all. I tried to use the code below, but it gives me partial matches - syllables instead of full words. #this is a code to recreate the

问题 I want to get a dataframe which included multiple subset from itself. For example: DataFrame(data = a[1,2,3,4,5,6,7,8,9]) . I want build a dataframe with iloc[0,3] and iloc[6:9] which resulting: DataFrame(data = a[1,2,3,6,7,8]) . Currently I am doing like this which is keep doing data copying and very slow: if my_df is not None: domain += 1 new_domain = df.iloc[begin_iloc: begin_of_next_iloc] new_domain['domain'] = domain my_df = my_df.append(new_domain) else: my_df = df.iloc[begin_iloc:

问题 I want to get a dataframe which included multiple subset from itself. For example: DataFrame(data = a[1,2,3,4,5,6,7,8,9]) . I want build a dataframe with iloc[0,3] and iloc[6:9] which resulting: DataFrame(data = a[1,2,3,6,7,8]) . Currently I am doing like this which is keep doing data copying and very slow: if my_df is not None: domain += 1 new_domain = df.iloc[begin_iloc: begin_of_next_iloc] new_domain['domain'] = domain my_df = my_df.append(new_domain) else: my_df = df.iloc[begin_iloc:

问题 There are a number of excellent answers to this question GUIs for displaying dataframes, but what I'm looking to do is a bit more advanced. I'd like to display a dataframe, but have a couple of the columns be interactive where the user can manually overwrite values (and the rest be static). It would be useful to have "total" rows that change with the overwritten values and eventually have some interactive buttons around the dataframe for loading and clearing data. QTPandas looks promising,

问题 Given a test dataset as follows: id city name 0 1 bj NaN 1 2 bj jack 2 3 bj NaN 3 4 bj jim 4 5 sh NaN 5 6 sh NaN 6 7 sh steve 7 8 sh fiona 8 9 sh NaN How could I groupby city and drop NaN rows for name while preserving one only for each group ? Many thanks. The expected result will like this: id city name 0 1 bj NaN 1 2 bj jack 2 4 bj jim 3 5 sh NaN 4 7 sh steve 5 8 sh fiona New dataset read by df = pd.read_clipboard(na_filter = False) from excel file, please note N/A should not be considered

问题 Given a test dataset as follows: id city name 0 1 bj NaN 1 2 bj jack 2 3 bj NaN 3 4 bj jim 4 5 sh NaN 5 6 sh NaN 6 7 sh steve 7 8 sh fiona 8 9 sh NaN How could I groupby city and drop NaN rows for name while preserving one only for each group ? Many thanks. The expected result will like this: id city name 0 1 bj NaN 1 2 bj jack 2 4 bj jim 3 5 sh NaN 4 7 sh steve 5 8 sh fiona New dataset read by df = pd.read_clipboard(na_filter = False) from excel file, please note N/A should not be considered