Python Pandas Cheat Sheet

%matplotlib inline	plots into notebook
df = pd.read_csv(path, index_col ='name')	loads dataframe
df.head()
df.tail()
df.values
df.plot() df.plot(style='.')
df.index	returns row indexes
df[col].loc[index]	returns value with given column and index
df.loc[:, 'col_n+1'] = x	referring to a col that doesn't exist creates a new one

df.isnull()	returns bool
df[.isnull().sum	returns sum of trues
df.isnull().any	checks whether there is a true
`df[col].` max() `df[col].` min()
`df[col].` idxmax() `df[col].` idxmin()
`df[col].` median()
`df[col].` mean()
`df[col].` describe()	gives statistic analysis
`df[col].` quantile(.5)	50% quantile
df.boxplot(by = 'col')	boxplot grouped by column
df.hist(bins = 20)	histogram in 20 bars
df.plot.scatter(x = 'name', y = 'name')	scatterplot
pd.plotting.scatter_matrix(df)	multiple scatterplots
pd.plotting.parallel_coordinates(df, 'name')	lines drawn connecting dimensions of an entry
df['col_name'].unique	returns list of singled entries
pd.get_dummies(df, columns=['Name'])	dummie column (0 or 1) that indicates whether the entry in another column is a certain entry
np.random.choice(n, x, replace=false)	selects random set
np.setdiff1d(set_1, set_2)	New set with only the differing entries
df.to_numpy()	gives array of entries

df['col1'] == x	bool if entry is x
df[df == x] = y	replace all values of a kind

df.loc[rowindex, columnname]
df.loc[3, col1] df.loc[3:6, ['col1', 'col2']]	3rd entry of 1st column
df.loc[:, 'col1' ] == 'name'	column with t/f whether entry in col1 is name

df.iloc[3:-1, 2:]	[rows, columns]
df.iloc[:, [3, 1]]	columns with index 3 & 1

.loc

is label based,

.iloc

is integer index based

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])	creates a pandas series
s1.add(s2, fill_value=0)
s.isnull() ; s.notnull()
s.dropna()	drops all rows with missing values
s.fillna(x)
s = pd.DataFrame({'Size':s1, 'Weight':s2})	Best way to define dataframe out of series: Give dict out of columns
'e' in s1	returns bool
s.name = 'str'	names series
s.index.name = 'str'	names index If s doesn't exist, this creates a df
s.columns['Red', 'Green'] s.columns.name = 'Color'
s.reindex[('m', 'n', 'o'], method = 'ffill')	ffill = forward fill

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Python Pandas Cheat Sheet (DRAFT) by Lasse1618