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@ -2,7 +2,7 @@
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## Basic Pandas Imports
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```py
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```py linenums="1"
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import numpy as np
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import pandas as pd
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from pandas import Series, DataFrame
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@ -13,7 +13,7 @@ from pandas import Series, DataFrame
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1-dimensional labelled array, axis label referred as INDEX.
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Index can contain repetitions.
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```py
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```py linenums="1"
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s = Series(data, index=index, name='name')
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# DATA: {python dict, ndarray, scalar value}
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# NAME: {string}
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@ -22,7 +22,7 @@ s = Series(dict) # Series created from python dict, dict keys become index valu
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### INDEXING / SELECTION / SLICING
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```py
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```py linenums="1"
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s['index'] # selection by index label
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s[condition] # return slice selected by condition
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s[ : ] # slice endpoint included
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@ -34,7 +34,7 @@ s[condition] = *value # modify slice by condition
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Missing data appears as NaN (Not a Number).
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```py
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```py linenums="1"
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pd.isnull(array) # return a Series index-bool indicating which indexes don't have data
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pd.notnull(array) # return a Series index-bool indicating which indexes have data
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array.isnull()
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@ -43,7 +43,7 @@ array.notnull()
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### SERIES ATTRIBUTES
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```py
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```py linenums="1"
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s.values # NumPy representation of Series
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s.index # index object of Series
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s.name = "Series name" # renames Series object
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@ -52,7 +52,7 @@ s.index.name = "index name" # renames index
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### SERIES METHODS
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```py
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```py linenums="1"
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pd.Series.isin(self, values) # boolean Series showing whether elements in Series matches elements in values exactly
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# Conform Series to new index, new object produced unless the new index is equivalent to current one and copy=False
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@ -80,7 +80,7 @@ pd.Series.value_counts(self, normalize=False, sort=True, ascending=False, bins=N
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2-dimensional labeled data structure with columns of potentially different types.
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Index and columns can contain repetitions.
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```py
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```py linenums="1"
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df = DataFrame(data, index=row_labels, columns=column_labels)
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# DATA: {list, dict (of lists), nested dicts, series, dict of 1D ndarray, 2D ndarray, DataFrame}
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# INDEX: {list of row_labels}
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@ -112,7 +112,7 @@ del df[col] # delete column
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### DATAFRAME ATTRIBUTES
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```py
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```py linenums="1"
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df.index # row labels
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df.columns # column labels
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df.values # NumPy representation of DataFrame
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@ -123,7 +123,7 @@ df.T # transpose
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### DATAFRAME METHODS
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```py
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```py linenums="1"
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pd.DataFrame.isin(self , values) # boolean DataFrame showing whether elements in DataFrame matches elements in values exactly
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# Conform DataFrame to new index, new object produced unless the new index is equivalent to current one and copy=False
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@ -146,7 +146,7 @@ Holds axis labels and metadata, immutable.
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### INDEX TYPES
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```py
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```py linenums="1"
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pd.Index # immutable ordered ndarray, sliceable. stores axis labels
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pd.Int64Index # special case of Index with purely integer labels
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pd.MultiIndex # multi-level (hierarchical) index object for pandas objects
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@ -156,7 +156,7 @@ pd.DatetimeIndex # nanosecond timestamps (uses Numpy datetime64)
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### INDEX ATTRIBUTERS
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```py
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```py linenums="1"
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pd.Index.is_monotonic_increasing # Return True if the index is monotonic increasing (only equal or increasing) values
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pd.Index.is_monotonic_decreasing # Return True if the index is monotonic decreasing (only equal or decreasing) values
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pd.Index.is_unique # Return True if the index has unique values.
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@ -165,7 +165,7 @@ pd.Index.hasnans # Return True if the index has NaNs
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### INDEX METHODS
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```py
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```py linenums="1"
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pd.Index.append(self, other) # append a collection of Index options together
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pd.Index.difference(self, other, sort=None) # set difference of two Index objects
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@ -197,7 +197,7 @@ Missing values propagate in arithmetic computations (NaN `<operator>` value = Na
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### ADDITION
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```py
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```py linenums="1"
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self + other
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pd.Series.add(self, other, fill_value=None) # add(), supports substitution of NaNs
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pd,Series.radd(self, other, fill_value=None) # radd(), supports substitution of NaNs
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@ -210,7 +210,7 @@ pd.DataFrame.radd(self, other, axis=columns, fill_value=None) # radd(), support
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### SUBTRACTION
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```py
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```py linenums="1"
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self - other
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pd.Series.sub(self, other, fill_value=None) # sub(), supports substitution of NaNs
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pd.Series.radd(self, other, fill_value=None) # radd(), supports substitution of NaNs
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@ -223,7 +223,7 @@ pd.DataFrame.rsub(self, other, axis=columns, fill_value=None) # rsub(), support
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### MULTIPLICATION
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```py
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```py linenums="1"
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self * other
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pd.Series.mul(self, other, fill_value=None) # mul(), supports substitution of NaNs
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pd.Series.rmul(self, other, fill_value=None) # rmul(), supports substitution of NaNs
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@ -236,7 +236,7 @@ pd.DataFrame.rmul(self, other, axis=columns, fill_value=None) # rmul(), support
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### DIVISION (float division)
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```py
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```py linenums="1"
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self / other
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pd.Series.div(self, other, fill_value=None) # div(), supports substitution of NaNs
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pd.Series.rdiv(self, other, fill_value=None) # rdiv(), supports substitution of NaNs
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@ -253,7 +253,7 @@ pd.DataFrame.rtruediv(self, other, axis=columns, fill_value=None) # rtruediv(),
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### FLOOR DIVISION
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```py
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```py linenums="1"
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self // other
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pd.Series.floordiv(self, other, fill_value=None) # floordiv(), supports substitution of NaNs
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pd.Series.rfloordiv(self, other, fill_value=None) # rfloordiv(), supports substitution of NaNs
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@ -266,7 +266,7 @@ pd.DataFrame.rfloordiv(self, other, axis=columns, fill_value=None) # rfloordiv(
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### MODULO
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```py
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```py linenums="1"
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self % other
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pd.Series.mod(self, other, fill_value=None) # mod(), supports substitution of NaNs
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pd.Series.rmod(self, other, fill_value=None) # rmod(), supports substitution of NaNs
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@ -279,7 +279,7 @@ pd.DataFrame.rmod(self, other, axis=columns, fill_value=None) # rmod(), support
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### POWER
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```py
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```py linenums="1"
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other ** self
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pd.Series.pow(self, other, fill_value=None) # pow(), supports substitution of NaNs
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pd.Series.rpow(self, other, fill_value=None) # rpow(), supports substitution of NaNs
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@ -296,7 +296,7 @@ pd.DataFrame.rpow(self, other, axis=columns, fill_value=None) # rpow(), support
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NumPy ufuncs work fine with pandas objects.
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```py
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```py linenums="1"
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pd.DataFrame.applymap(self, func) # apply function element-wise
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pd.DataFrame.apply(self, func, axis=0, args=()) # apply a function along an axis of a DataFrame
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@ -324,7 +324,7 @@ pd.DataFrame.sort_values(self, axis=0, ascending=True, **kwargs) # sort object
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### COUNT
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```py
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```py linenums="1"
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pd.Series.count(self) # return number of non-NA/null observations in the Series
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pd.DataFrame.count(self, numeric_only=False) # count non-NA cells for each column or row
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# NUMERIC_ONLY: {bool} -- Include only float, int or boolean data -- DEFAULT False
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Generate descriptive statistics summarizing central tendency, dispersion and shape of dataset's distribution (exclude NaN).
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```py
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```py linenums="1"
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pd.Series.describe(self, percentiles=None, include=None, exclude=None)
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pd.DataFrame.describe(self, percentiles=None, include=None, exclude=None)
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# PERCENTILES: {list-like of numbers} -- percentiles to include in output,between 0 and 1 -- DEFAULT [.25, .5, .75]
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### MAX - MIN
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```py
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```py linenums="1"
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pd.Series.max(self, skipna=None, numeric_only=None) # maximum of the values for the requested axis
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pd.Series.min(self, skipna=None, numeric_only=None) # minimum of the values for the requested axis
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pd.DataFrame.max(self, axis=None, skipna=None, numeric_only=None) # maximum of the values for the requested axis
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@ -355,7 +355,7 @@ pd.DataFrame.min(self, axis=None, skipna=None, numeric_only=None) # minimum of
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### IDXMAX - IDXMIN
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```py
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```py linenums="1"
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pd.Series.idxmax(self, skipna=True) # row label of the maximum value
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pd.Series.idxmin(self, skipna=True) # row label of the minimum value
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pd.DataFrame.idxmax(self, axis=0, skipna=True) # Return index of first occurrence of maximum over requested axis
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@ -366,7 +366,7 @@ pd.DataFrame.idxmin(self, axis=0, skipna=True) # Return index of first occurre
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### QUANTILE
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```py
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```py linenums="1"
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pd.Series.quantile(self, q=0.5, interpolation='linear') # return values at the given quantile
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pd.DataFrame.quantile(self, q=0.5, axis=0, numeric_only=True, interpolation='linear') # return values at the given quantile over requested axis
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# Q: {flaot, array} -- value between 0 <= q <= 1, the quantile(s) to compute -- DEFAULT 0.5 (50%)
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### SUM
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```py
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```py linenums="1"
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pd.Series.sum(self, skipna=None, numeric_only=None, min_count=0) # sum of the values
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pd.DataFrame.sum(self, axis=None, skipna=None, numeric_only=None, min_count=0) # sum of the values for the requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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@ -387,7 +387,7 @@ pd.DataFrame.sum(self, axis=None, skipna=None, numeric_only=None, min_count=0)
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### MEAN
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```py
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```py linenums="1"
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pd.Series.mean(self, skipna=None, numeric_only=None) # mean of the values
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pd.DataFrame.mean(self, axis=None, skipna=None, numeric_only=None) # mean of the values for the requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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@ -397,7 +397,7 @@ pd.DataFrame.mean(self, axis=None, skipna=None, numeric_only=None) # mean of th
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### MEDIAN
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```py
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```py linenums="1"
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pd.Series.median(self, skipna=None, numeric_only=None) # median of the values
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pd.DataFrame.median(self, axis=None, skipna=None, numeric_only=None) # median of the values for the requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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### MAD (mean absolute deviation)
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```py
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```py linenums="1"
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pd.Series.mad(self, skipna=None) # mean absolute deviation
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pd.DataFrame.mad(self, axis=None, skipna=None) # mean absolute deviation of the values for the requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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### VAR (variance)
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```py
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```py linenums="1"
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pd.Series.var(self, skipna=None, numeric_only=None) # unbiased variance
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pd.DataFrame.var(self, axis=None, skipna=None, ddof=1, numeric_only=None) # unbiased variance over requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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### STD (standard deviation)
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```py
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```py linenums="1"
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pd.Series.std(self, skipna=None, ddof=1, numeric_only=None) # sample standard deviation
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pd.Dataframe.std(self, axis=None, skipna=None, ddof=1, numeric_only=None) # sample standard deviation over requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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### SKEW
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```py
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```py linenums="1"
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pd.Series.skew(self, skipna=None, numeric_only=None) # unbiased skew Normalized bt N-1
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pd.DataFrame.skew(self, axis=None, skipna=None, numeric_only=None) # unbiased skew over requested axis Normalized by N-1
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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@ -450,7 +450,7 @@ pd.DataFrame.skew(self, axis=None, skipna=None, numeric_only=None) # unbiased
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Unbiased kurtosis over requested axis using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
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```py
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```py linenums="1"
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pd.Series.kurt(self, skipna=None, numeric_only=None)
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pd.Dataframe.kurt(self, axis=None, skipna=None, numeric_only=None)
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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### CUMSUM (cumulative sum)
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```py
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```py linenums="1"
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pd.Series.cumsum(self, skipna=True) # cumulative sum
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pd.Dataframe.cumsum(self, axis=None, skipna=True) # cumulative sum over requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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@ -469,7 +469,7 @@ pd.Dataframe.cumsum(self, axis=None, skipna=True) # cumulative sum over request
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### CUMMAX - CUMMIN (cumulative maximum - minimum)
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```py
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```py linenums="1"
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pd.Series.cummax(self, skipna=True) # cumulative maximum
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pd.Series.cummin(self, skipna=True) # cumulative minimum
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pd.Dataframe.cummax(self, axis=None, skipna=True) # cumulative maximum over requested axis
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@ -480,7 +480,7 @@ pd.Dataframe.cummin(self, axis=None, skipna=True) # cumulative minimum over req
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### CUMPROD (cumulative product)
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```py
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```py linenums="1"
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pd.Series.cumprod(self, skipna=True) # cumulative product
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pd.Dataframe.cumprod(self, axis=None, skipna=True) # cumulative product over requested axis
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# AXIS: {0, 1, index, columns} -- axis for the function to be applied on
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@ -492,7 +492,7 @@ pd.Dataframe.cumprod(self, axis=None, skipna=True) # cumulative product over re
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Calculates the difference of a DataFrame element compared with another element in the DataFrame.
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(default is the element in the same column of the previous row)
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```py
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```py linenums="1"
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pd.Series.diff(self, periods=1)
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pd.DataFrame.diff(self, periods=1, axis=0)
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# PERIODS: {int} -- Periods to shift for calculating difference, accepts negative values -- DEFAULT 1
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@ -503,7 +503,7 @@ pd.DataFrame.diff(self, periods=1, axis=0)
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Percentage change between the current and a prior element.
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```py
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```py linenums="1"
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pd.Series.Pct_change(self, periods=1, fill_method='pad', limit=None, freq=None)
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pd.Dataframe.pct_change(self, periods=1, fill_method='pad', limit=None)
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# PERIODS:{int} -- periods to shift for forming percent change
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@ -515,7 +515,7 @@ pd.Dataframe.pct_change(self, periods=1, fill_method='pad', limit=None)
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### FILTERING OUT MISSING DATA
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```py
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```py linenums="1"
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pd.Series.dropna(self, inplace=False) # return a new Series with missing values removed
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pd.DataFrame.dropna(axis=0, how='any', tresh=None, subset=None, inplace=False) # return a new DataFrame with missing values removed
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# AXIS: {tuple, list} -- tuple or list to drop on multiple axes. only a single axis is allowed
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@ -529,7 +529,7 @@ pd.DataFrame.dropna(axis=0, how='any', tresh=None, subset=None, inplace=False)
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Fill NA/NaN values using the specified method.
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```py
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```py linenums="1"
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pd.Series.fillna(self, value=None, method=None, inplace=False, limit=None)
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pd.DataFrame.fillna(self, value=None, method=None, axis=None, inplace=False, limit=None)
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# VALUE: {scalar, dict, Series, DataFrame} -- value to use to fill holes, dict/Series/DataFrame specifying which value to use for each index or column
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@ -546,7 +546,7 @@ In lower dimensional data structures like Series (1d) and DataFrame (2d).
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### MULTIIINDEX CREATION
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```py
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```py linenums="1"
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pd.MultiIndex.from_arrays(*arrays, names=None) # convert arrays to MultiIndex
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pd.MultiIndex.from_tuples(*arrays, names=None) # convert tuples to MultiIndex
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pd.MultiIndex.from_frame(df, names=None) # convert DataFrame to MultiIndex
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@ -559,7 +559,7 @@ pd.DataFrame(*arrays) # Index constructor makes MultiINdex from DataFrame
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Vector of label values for requested level, equal to the length of the index.
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```py
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```py linenums="1"
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pd.MultiIndex.get_level_values(self, level)
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```
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@ -567,7 +567,7 @@ pd.MultiIndex.get_level_values(self, level)
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|
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Partial selection "drops" levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame.
|
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|
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```py
|
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```py linenums="1"
|
||||
pd.Series.xs(self, key, axis=0, level=None, drop_level=True) # cross-section from Series
|
||||
pd.DataFrame.xs(self, key, axis=0, level=None, drop_level=True) # cross-section from DataFrame
|
||||
# KEY: {label, tuple of label} -- label contained in the index, or partially in a MultiIndex
|
||||
|
|
@ -580,7 +580,7 @@ pd.DataFrame.xs(self, key, axis=0, level=None, drop_level=True) # cross-sectio
|
|||
|
||||
Multi index keys take the form of tuples.
|
||||
|
||||
```py
|
||||
```py linenums="1"
|
||||
df.loc[('lvl_1', 'lvl_2', ...)] # selection of single row
|
||||
df.loc[('idx_lvl_1', 'idx_lvl_2', ...), ('col_lvl_1', 'col_lvl_2', ...)] # selection of single value
|
||||
|
||||
|
|
@ -591,7 +591,7 @@ df.loc[('idx_lvl_1', 'idx_lvl_2') : ('idx_lvl_1', 'idx_lvl_2')] # slice of rows
|
|||
|
||||
### REORDERING AND SORTING LEVELS
|
||||
|
||||
```py
|
||||
```py linenums="1"
|
||||
pd.MultiIndex.swaplevel(self, i=-2, j=-1) # swap level i with level j
|
||||
pd.Series.swaplevel(self, i=-2, j=-1) # swap levels i and j in a MultiIndex
|
||||
pd.DataFrame.swaplevel(self, i=-2, j=-1, axis=0) # swap levels i and j in a MultiIndex on a partivular axis
|
||||
|
|
@ -604,7 +604,7 @@ pd.MultiIndex.sortlevel(self, level=0, ascending=True, sort_remaining=True) # s
|
|||
|
||||
## DATA LOADING, STORAGE FILE FORMATS
|
||||
|
||||
```py
|
||||
```py linenums="1"
|
||||
pd.read_fwf(filepath, colspecs='infer', widths=None, infer_nrows=100) # read a table of fixed-width formatted lines into DataFrame
|
||||
# FILEPATH: {str, path object} -- any valid string path is acceptable, could be a URL. Valid URLs: http, ftp, s3, and file
|
||||
# COLSPECS: {list of tuple (int, int), 'infer'} -- list of tuples giving extents of fixed-width fields of each line as half-open intervals { [from, to) }
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue