Label vs. Location The main distinction between the two methods is: loc gets rows (and/or columns) with particular labels. iloc gets rows (and/or columns) at integer locations. To demonstrate, consider a series s of characters with a non-monotonic integer index: >>> s = pd.Series(list(“abcdef”), index=[49, 48, 47, 0, 1, 2]) 49 a 48 b 47 … Read more
If both columns are strings, you can concatenate them directly: df[“period”] = df[“Year”] + df[“quarter”] If one (or both) of the columns are not string typed, you should convert it (them) first, df[“period”] = df[“Year”].astype(str) + df[“quarter”] Beware of NaNs when doing this! If you need to join multiple string columns, you can use agg: … Read more
See my comment in @gsk3 answer. A simple example: > m <- matrix(sample(c(NA, 1:10), 100, replace = TRUE), 10) > d <- as.data.frame(m) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 1 4 3 NA 3 7 6 6 10 6 5 2 9 8 9 5 10 NA 2 1 7 2 … Read more
To delimit by a tab you can use the sep argument of to_csv: df.to_csv(file_name, sep=’\t’) To use a specific encoding (e.g. ‘utf-8’) use the encoding argument: df.to_csv(file_name, sep=’\t’, encoding=’utf-8′)
You can use a simple list of names : DF <- data.frame( x=1:10, y=10:1, z=rep(5,10), a=11:20 ) drops <- c(“x”,”z”) DF[ , !(names(DF) %in% drops)] Or, alternatively, you can make a list of those to keep and refer to them by name : keeps <- c(“y”, “a”) DF[keeps] EDIT : For those still not acquainted … Read more
Also check complete.cases : > final[complete.cases(final), ] gene hsap mmul mmus rnor cfam 2 ENSG00000199674 0 2 2 2 2 6 ENSG00000221312 0 1 2 3 2 na.omit is nicer for just removing all NA‘s. complete.cases allows partial selection by including only certain columns of the dataframe: > final[complete.cases(final[ , 5:6]),] gene hsap mmul mmus … Read more
If ds is a list of dicts: df = pd.DataFrame(ds) Note: this does not work with nested data.
You can also use the option_context, with one or more options: with pd.option_context(‘display.max_rows’, None, ‘display.max_columns’, None): # more options can be specified also print(df) This will automatically return the options to their previous values. If you are working on jupyter-notebook, using display(df) instead of print(df) will use jupyter rich display logic (like so).
You can use the isin method: In [1]: df = pd.DataFrame({‘A’: [5,6,3,4], ‘B’: [1,2,3,5]}) In [2]: df Out[2]: A B 0 5 1 1 6 2 2 3 3 3 4 5 In [3]: df[df[‘A’].isin([3, 6])] Out[3]: A B 1 6 2 2 3 3 And to get the opposite use ~: In [4]: df[~df[‘A’].isin([3, … Read more
The SettingWithCopyWarning was created to flag potentially confusing “chained” assignments, such as the following, which does not always work as expected, particularly when the first selection returns a copy. [see GH5390 and GH5597 for background discussion.] df[df[‘A’] > 2][‘B’] = new_val # new_val not set in df The warning offers a suggestion to rewrite as … Read more