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How do I get the row count of a Pandas DataFrame? could use df info () so you get row count (# entries), number of non-null entries in each column, dtypes and memory usage Good complete picture of the df If you're looking for a number you can use programatically then df shape [0]
Selecting multiple columns in a Pandas dataframe - Stack Overflow So your column is returned by df['index'] and the real DataFrame index is returned by df index An Index is a special kind of Series optimized for lookup of its elements' values For df index it's for looking up rows by their label That df columns attribute is also a pd Index array, for looking up columns by their labels
How can I iterate over rows in a Pandas DataFrame? I have a pandas dataframe, df: c1 c2 0 10 100 1 11 110 2 12 120 How do I iterate over the rows of this dataframe? For every row, I want to access its elements (values in cells) by the n
How do I select rows from a DataFrame based on column values? Only, when the size of the dataframe approaches million rows, many of the methods tend to take ages when using df[df['col']==val] I wanted to have all possible values of "another_column" that correspond to specific values in "some_column" (in this case in a dictionary)
why should I make a copy of a data frame in pandas So any changes made to df` or df2 will be made to the same object instance Whereas in the df2 = df copy() a second object instance is created, a copy of the first one, but now df and df2 reference to different object instances and any changes will be made to their respective DataFrame instance
Import CSV file as a Pandas DataFrame - Stack Overflow To read a CSV file as a pandas DataFrame, you'll need to use pd read_csv, which has sep=',' as the default But this isn't where the story ends; data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly Here's a table listing common scenarios encountered with CSV files along
python - Shuffle DataFrame rows - Stack Overflow Doesn't df = df sample(frac=1) do the exact same thing as df = sklearn utils shuffle(df)? According to my measurements df = df sample(frac=1) is faster and seems to perform the exact same action They also both allocate new memory np random shuffle(df values) is the slowest, but does not allocate new memory