I have a number of columns that I would like to remove from a data frame. I know that we can delete them individually using something like: df$x <- NULL But I was hoping to do this with fewer commands. Also, I know that I could drop columns using integer indexing like this: df <- … Read more
I work with Series and DataFrames on the terminal a lot. The default __repr__ for a Series returns a reduced sample, with some head and tail values, but the rest missing. Is there a builtin way to pretty-print the entire Series / DataFrame? Ideally, it would support proper alignment, perhaps borders between columns, and maybe … Read more
I’d like to remove the lines in this data frame that: a) contain NAs across all columns. Below is my example data frame. gene hsap mmul mmus rnor cfam 1 ENSG00000208234 0 NA NA NA NA 2 ENSG00000199674 0 2 2 2 2 3 ENSG00000221622 0 NA NA NA NA 4 ENSG00000207604 0 NA NA … Read more
I have a list of dictionaries like this: [{‘points’: 50, ‘time’: ‘5:00’, ‘year’: 2010}, {‘points’: 25, ‘time’: ‘6:00’, ‘month’: “february”}, {‘points’:90, ‘time’: ‘9:00’, ‘month’: ‘january’}, {‘points_h1’:20, ‘month’: ‘june’}] And I want to turn this into a pandas DataFrame like this: month points points_h1 time year 0 NaN 50 NaN 5:00 2010 1 february 25 NaN … Read more
Let’s say I have the following Pandas dataframe: df = DataFrame({‘A’ : [5,6,3,4], ‘B’ : [1,2,3, 5]}) df A B 0 5 1 1 6 2 2 3 3 3 4 5 I can subset based on a specific value: x = df[df[‘A’] == 3] x A B 2 3 3 But how can I … Read more
Background I just upgraded my Pandas from 0.11 to 0.13.0rc1. Now, the application is popping out many new warnings. One of them like this: E:\FinReporter\FM_EXT.py:449: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead quote_df[‘TVol’] = quote_df[‘TVol’]/TVOL_SCALE I want to know … Read more
I have this DataFrame and want only the records whose EPS column is not NaN: >>> df STK_ID EPS cash STK_ID RPT_Date 601166 20111231 601166 NaN NaN 600036 20111231 600036 NaN 12 600016 20111231 600016 4.3 NaN 601009 20111231 601009 NaN NaN 601939 20111231 601939 2.5 NaN 000001 20111231 000001 NaN NaN …i.e. something like … Read more
I have the following indexed DataFrame with named columns and rows not- continuous numbers: a b c d 2 0.671399 0.101208 -0.181532 0.241273 3 0.446172 -0.243316 0.051767 1.577318 5 0.614758 0.075793 -0.451460 -0.012493 I would like to add a new column, ‘e’, to the existing data frame and do not want to change anything in … Read more
How do I create an empty DataFrame, then add rows, one by one? I created an empty DataFrame: df = pd.DataFrame(columns=(‘lib’, ‘qty1’, ‘qty2’)) Then I can add a new row at the end and fill a single field with: df = df._set_value(index=len(df), col=”qty1″, value=10.0) It works for only one field at a time. What is … Read more
I want to convert a table, represented as a list of lists, into a pandas DataFrame. As an extremely simplified example: a = [[‘a’, ‘1.2’, ‘4.2’], [‘b’, ’70’, ‘0.03’], [‘x’, ‘5’, ‘0’]] df = pd.DataFrame(a) What is the best way to convert the columns to the appropriate types, in this case columns 2 and 3 … Read more
