====== Basic Pandas ======

===== Basic navigation =====

See the first 3 rows: ''df.head(3)''

Valores únicos de una columna ''df.<columna>.unique()''

Coger solo unas columnas: 
<code>df[['1990', '2000', '2010']]</code>

Filter by a row value:
<code>
is_1809 =  df['Mes'] == 'Septiembre 18'
df[['Mes', 'Categoria', 'Real']][is_1809]
</code>

==== To access data ====

''DataFrame.at'' Access a single value for a row/column label pair

''DataFrame.iloc'' Access group of rows and columns by integer position(s)


''DataFrame.xs'' Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.

''Series.loc'' Access group of values using labels
===== Basic operations =====

Remove column from dataframe: ''df.drop('column_name', axis=1)''. Also: ''df.drop(columns=['Esperado acumulado'])''

Remove row by index: ''df.drop([0, 1])''

Eliminar una row cuando contenga valor en una columna (Categoria) vacío:
<code>
df = df[df['Categoria'] != '']
</code>

==== Pivot table ====
<code>
pd.pivot_table(df, index=['Mes'], columns=['Categoria'], values=['Real'], aggfunc='sum')
</code>

==== Change order ====

Re-Organize indexes:
<code>
df.reindex(['Enero', 'Febrero', 'Marzo'])
</code>

==== Stack and Unstack ====

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