Cheat Sheet Which Includes Syntax

Data Wrangling with pandas Cheat Sheet Tidy Data – A foundation for wrangling in pandas F M A In a tidy data set: & Each variable is saved in its own column b c 1 4 7 10 2 5 8 11 3 6 9 12 b c 1 4 7 10 2 5 8 11 2 6 9 12 M M Each observation is saved in its own row * A df = pd.DataFrame( {"a" : [4 ,5, 6], "b" : [7, 8, 9], "c" : [10, 11, 12]}, index = pd.MultiIndex.from_tuples( [('d',1),('d',2),('e',2)], names=['n','v'])) Create DataFrame with a MultiIndex Method Chaining Most pandas methods return a DataFrame so that another pandas method can be applied to the result. This improves readability of code. df = (pd.melt(df) F *A pd.melt(df) Gather columns into rows. df.pivot(columns='var', values='val') Spread rows into columns. df.rename(columns = {'y':'year'}) Rename the columns of a DataFrame df.sort_index() Sort the index of a DataFrame df.reset_index() Reset index of DataFrame to row numbers, moving index to columns. pd.concat([df1,df2]) Append rows of DataFrames pd.concat([df1,df2], axis=1) Append columns of DataFrames Subset Observations (Rows) df.drop(columns=['Length','Height']) Drop columns from DataFrame Subset Variables (Columns) v d e Tidy data complements pandas’s vectorized operations. pandas will automatically preserve observations as you manipulate variables. No other format works as intuitively with pandas. df.sort_values('mpg',ascending=False) Order rows by values of a column (high to low). df = pd.DataFrame( [[4, 7, 10], [5, 8, 11], [6, 9, 12]], index=[1, 2, 3], columns=['a', 'b', 'c']) Specify values for each row. a A df.sort_values('mpg') Order rows by values of a column (low to high). df = pd.DataFrame( {"a" : [4 ,5, 6], "b" : [7, 8, 9], "c" : [10, 11, 12]}, index = [1, 2, 3]) Specify values for each column. n M Reshaping Data – Change the layout of a data set Syntax – Creating DataFrames a F df[df.Length > 7] Extract rows that meet logical criteria. df.drop_duplicates() Remove duplicate rows (only considers columns). df.head(n) Select first n rows. df.tail(n) Select last n rows. df.sample(frac=0.5) Randomly select fraction of rows. df.sample(n=10) Randomly select n rows. df.iloc[10:20] Select rows by position. df.nlargest(n, 'value') Select and order top n entries. df.nsmallest(n, 'value') Select and order bottom n entries. Logic in Python (and pandas) < Less than != Not equal to df[['width','length','species']] Select multiple columns with specific names. df['width'] or df.width Select single column with specific name. df.filter(regex='regex') Select columns whose name matches regular expression regex. regex (Regular Expressions) Examples '\.' Matches strings containing a period '.' 'Length$' Matches strings ending with word 'Length' '^Sepal' Matches strings beginning with the word 'Sepal' '^x[1-5]$' Matches strings beginning with 'x' and ending with 1,2,3,4,5 '^(?!Species$).*' Matches strings except the string 'Species' df.loc[:,'x2':'x4'] .rename(columns={ 'variable' : 'var', 'value' : 'val'}) .query('val >= 200') ) > Greater than == Equals df.column.isin (values) pd.isnull(obj) Group membership Is NaN <= Less than or equals pd.notnull (obj) Is not NaN >= Greater than or equals &,|,~,^, df.any(),df.all() Logical and, or, not, xor, any, all Select all columns between x2 and x4 (inclusive). df.iloc[:,[1,2,5]] Select columns in positions 1, 2 and 5 (first column is 0). df.loc[df['a'] > 10, ['a','c']] Select rows meeting logical condition, and only the specific columns . Summarize Data df['w'].value_counts() Count number of rows with each unique value of variable len(df) # of rows in DataFrame. df['w'].nunique() # of distinct values in a column. df.describe() Basic descriptive statistics for each column (or GroupBy) pandas provides a large set of summary functions that operate on different kinds of pandas objects (DataFrame columns, Series, GroupBy, Expanding and Rolling (see below)) and produce single values for each of the groups. When applied to a DataFrame, the result is returned as a pandas Series for each column. Examples: sum() Sum values of each object. count() Count non-NA/null values of each object. median() Median value of each object. quantile([0.25,0.75]) Quantiles of each object. apply(function) Apply function to each object. min() Minimum value in each object. max() Maximum value in each object. mean() Mean value of each object. var() Variance of each object. std() Standard deviation of each object. Group Data df.groupby(by="col") Return a GroupBy object, grouped by values in column named "col". df.groupby(level="ind") Return a GroupBy object, grouped by values in index level named "ind". All of the summary functions listed above can be applied to a group. Additional GroupBy functions: size() agg(function) Size of each group. Aggregate group using function. Windows df.expanding() Return an Expanding object allowing summary functions to be Combine Data Sets Handling Missing Data adf df.dropna() Drop rows with any column having NA/null data. df.fillna(value) Replace all NA/null data with value. Make New Columns x1 A B C Vector function Vector function pandas provides a large set of vector functions that operate on all columns of a DataFrame or a single selected column (a pandas Series). These functions produce vectors of values for each of the columns, or a single Series for the individual Series. Examples: min(axis=1) max(axis=1) Element-wise min. Element-wise max. clip(lower=-10,upper=10) abs() Trim values at input thresholds Absolute value. The examples below can also be applied to groups. In this case, the function is applied on a per-group basis, and the returned vectors are of the length of the original DataFrame. shift(1) Copy with values shifted by 1. rank(method='dense') Ranks with no gaps. rank(method='min') Ranks. Ties get min rank. rank(pct=True) Ranks rescaled to interval [0, 1]. rank(method='first') Ranks. Ties go to first value. shift(-1) Copy with values lagged by 1. cumsum() Cumulative sum. cummax() Cumulative max. cummin() Cumulative min. cumprod() Cumulative product. Plotting df.plot.hist() Histogram for each column df.plot.scatter(x='w',y='h') Scatter chart using pairs of points bdf x1 A B D x3 T F T Standard Joins x1 A B C df.assign(Area=lambda df: df.Length*df.Height) Compute and append one or more new columns. df['Volume'] = df.Length*df.Height*df.Depth Add single column. pd.qcut(df.col, n, labels=False) Bin column into n buckets. x2 1 2 3 x2 x3 pd.merge(adf, bdf, 1 T how='left', on='x1') 2 F Join matching rows from bdf to adf. 3 NaN x1 x2 A 1.0 B 2.0 D NaN x3 T F T x1 A B x3 pd.merge(adf, bdf, T how='inner', on='x1') F Join data. Retain only rows in both sets. x2 1 2 pd.merge(adf, bdf, how='right', on='x1') Join matching rows from adf to bdf. x1 x2 x3 pd.merge(adf, bdf, A 1 T how='outer', on='x1') B 2 F Join data. Retain all values, all rows. C 3 NaN D NaN T Filtering Joins adf[adf.x1.isin(bdf.x1)] x1 x2 All rows in adf that have a match in bdf. A 1 B 2 adf[~adf.x1.isin(bdf.x1)] All rows in adf that do not have a match in bdf. x1 x2 C 3 ydf zdf x1 A B C x1 B C D x2 1 2 3 x2 2 3 4 Set-like Operations x1 x2 B 2 C 3 pd.merge(ydf, zdf) Rows that appear in both ydf and zdf (Intersection). x1 A B C D pd.merge(ydf, zdf, how='outer') Rows that appear in either or both ydf and zdf (Union). x2 1 2 3 4 pd.merge(ydf, zdf, how='outer', applied cumulatively. df.rolling(n) Return a Rolling object allowing summary functions to be applied to windows of length n. x1 x2 A 1 indicator=True) .query('_merge == "left_only"') .drop(columns=['_merge']) Rows that appear in ydf but not zdf (Setdiff).