Why even wait for autocompletion when you can use pandas_shortcuts?
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Simply import
pandas_shortcutstogether withpandas.import pandas as pd import pandas_shortcuts
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Every
pd.DataFrameandpd.Seriesobjects will have:- Shortcuts (full list below)
# shortcut for `df.head()` df.h() # shortcut for df.columns df.c # shortcut for df["col"].unique() df["col"].u()
- New methods (full list below)
# view up to `r` rows and `c` columns of a dataframe, overriding pandas' default limit df.v() # default r=50, c=50 # view up to `r` rows of a series, overriding pandas' default limit df["col"].v(100) # stylize a dataframe's numeric columns as heatmap or bars # view up to `r` rows and `c` of a dataframe, overriding pandas' default limit df.sh() # style=heatmap df.sb() # style=bar
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df.v()directly generatesIPython.core.display.HTMLobject under the hood, thus completely bypassing anypd.set_option("display.max_rows", ...)andpd.set_option("display.max_columns", ...)that the user may have specified.
Top Level API
pd.df # pd.DataFrame
# IO
pd.csv # pd.read_csv
pd.json # pd.read_json
pd.parquet # pd.read_parquet
pd.sql # pd.read_sql
pd.xlsx # pd.read_excel
# General function - Pivot
pd.pv # pd.pivot
pd.pvt # pd.pivot_table
# General function - datetime
pd.tdt # pd.to_datetime
pd.ttd # pd.to_timedeltaDataframe API
# Reindexing / selection / label manipulation
df.f2 # df.rename
## Heads or tails
df.h # df.head
df.t # df.tail
## Duplicates
df.dd # df.drop_duplicates
df.dup # df.duplicated
## Index
df.sx # df.set_index
df.rx # df.reset_index
# Reshaping, Sorting, Transposing
## Sort
df.si # df.sort_index
df.sv # df.sort_values
## Pivot
df.pv # df.pivot
df.pvt # df.pivot_table
# Groupby
df.gb # df.groupby
# Missing data handling
df.dna # df.dropna
df.fna # df.fillna
# Computations / descriptive stats
df.desc # df.describe
df.vc # df.cv # df.value_counts
df.nu # df.nunique
# Properties
df.c # df.columns
df.i # df.index
# IO
df.cb # df.to_clipboard
df.dict # df.to_dict
df.np # df.to_numpy
## File types
df.csv # df.to_csv
df.html # df.to_html
df.json # df.to_json
df.md # df.to_markdown
df.parquet # df.to_parquet
df.xlsx # df.to_excelSeries API
# Reindexing / selection / label manipulation
## Heads or tails
df["col"].h # df["col"].head
df["col"].t # df["col"].tail
## Duplicates
df["col"].dd # df["col"].drop_duplicates
df["col"].dup # df["col"].duplicated
## Index
df["col"].rx # df["col"].reset_index
# Reshaping, Sorting, Transposing
## Sort
df["col"].si # df["col"].sort_index
df["col"].sv # df["col"].sort_values
# Groupby
df["col"].gb # df["col"].groupby
# Missing data handling
df["col"].dna # df["col"].dropna
df["col"].fna # df["col"].fillna
# Computations / descriptive stats
df["col"].vc # df["col"].cv # df["col"].value_counts
df["col"].nu # df["col"].nunique
df["col"].u # df["col"].unique
# Properties
df["col"].i # df["col"].index
# IO
df["col"].cb # df["col"].to_clipboard
df["col"].dict # df["col"].to_dict
df["col"].list # df["col"].to_list
df["col"].np # df["col"].to_numpy
## File types
df["col"].csv # df["col"].to_csv
df["col"].json # df["col"].to_json
df["col"].md # df["col"].to_markdown
df["col"].xlsx # df["col"].to_excelMethods
df.sh # style_heatmap
df.sb # style_bar
df.v # dataframe_view
df["col"].v # series_view
