This is a summary of basic functions and concepts from pandas tutorial for new users, focusing on creating dataFrame, grouping, IO, lambda, selecting data and handling indexs etc the very necessity of using pandas.
I first list the functions together to give a overview :
list zip list(zip(names, numbers)) pd.DataFrame(data = , columns=[]) df.to_csv pd.read_csv df.types (check datatype of column) df.sort_values([column_name], ascending = True or False ) df.sort_index(axis=0)
random.seed() random.randint(low = , high = )
random_names = [names[random.randint(low=0 high = len(names))] for i in range(1000)]
len range df.info() df.describe() df[column_name].unique()
df.groupby('index') df.groupby().sum()
def function(): return
pd.read_excel(path, index_col = ) df.index
df['State'] = df.State.apply(lambda x: x.upper()) df = df[df['Status'] == 1] boolean indexing
df.reset_index() Daily = df.reset_index().groupby(['State','StatusDate']).sum()
del df[column_name] df.index.levels[0]
StateYearMonth = Daily.groupby([Daily.index.get_level_values(0), Daily.index.get_level_values(1).year, Daily.index.get_level_values(1).month]) Daily['Lower'] = StateYearMonth['CustomerCount'].transform(lambda x: x.quantile(q=.25) - (1.5x.quantile(q=.75) - x.quantile(q=.25)))* YearMonth = All.groupby([lambda x: x.year, lambda x:x.month])
idx = pd.date_range(start = , end = , freq= ) df = pd.DataFrame(data, index = idx, columns = [columns_name]) pd.concat() Year = combined.gropupby(lambda x:x.year).max() df.pct_change(periods = )
df.iloc[0:3] df[column_name] df.loc[df.index[0:3], column_name]
d = {'one':[1,1], 'two':[2,2]} i = ['a', 'b'] df = pd.DataFrame(data = d, index = i)
one two a 1 2 b 1 2
df.stack() a one 1 two 2 b one 1 two 2
df.unstack() one a 1 b 1 two a 2 b 2
df.T a b one 1 1 two 2 2
end
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