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df[df["cost"].eq(250)] cost revenue A 250 100 Compare DataFrames for greater than inequality or equality elementwise. df[df["cost"].ge(100)] cost revenue A 250 100 B 150 250 C 100 300 Compare DataFrames for strictly less than inequality elementwise.
Of the three methods above, len(df.index) (as mentioned in other answers) is the fastest. Note. All the methods above are constant time operations as they are simple attribute lookups. df.shape (similar to ndarray.shape) is an attribute that returns a tuple of (# Rows, # Cols). For example, df.shape returns (8, 2) for the example here.
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?
newdf = df[df.columns[2:4]] # Remember, Python is zero-offset! The "third" entry is at slot two. As EMS points out in his answer, df.ix slices columns a bit more concisely, but the .columns slicing interface might be more natural, because it uses the vanilla one-dimensional Python list indexing/slicing syntax.
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To delete a column in a DataFrame, I can successfully use: del df['column_name'] But why can't I use the following? del df.column_name Since it is possible to access the Series via df.column_name...
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. –
df_all = pd.read_csv('data.csv', error_bad_lines=False, chunksize=1000000) urls = pd.read_excel('url_june.xlsx') substr = urls.url.values.tolist() df_res = pd.DataFrame() for df in df_all: for i in substr: res = df[df['url'].str.contains(i)] df_res.append(res) And when I try to save df_res I get empty dataframe. df_all looks like
1st - pd.concat() + list %%timeit df = pd.DataFrame(columns=['a', 'b']) for i in range(10000): df = pd.concat([pd.DataFrame([[1,2]], columns=df.columns), df], ignore ...
df = pd.DataFrame(columns=['A', 'B', 'C'], index=range(5)) df A B C 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN It creates a DataFrame of object columns, like the others. df.dtypes A object # you DON'T want this B object C object dtype: object