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Sys.setenv(RETICULATE_PYTHON = "/Library/Frameworks/Python.framework/Versions/3.11/bin/python3.11")
library(reticulate)
use_python("/Library/Frameworks/Python.framework/Versions/3.11/bin/python3.11")Data manipulation with Pandas
1 Set up Python:Using python 3.11 instead of 3.13
because siuba only work with python 3.11 for now
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repl_python()exitCode
from platform import python_version
print(python_version())3.11.9
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Show package’s dependencies
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import os
#os.system('pip show pandas')
from pip._vendor import pkg_resources
_package_name = 'pandas'
_package = pkg_resources.working_set.by_key[_package_name]
print([str(r) for r in _package.requires()]) # retrieve deps from setup.py['python-dateutil>=2.8.2', 'pytz>=2020.1', 'tzdata>=2022.1', 'numpy>=1.21.0; python_version >= "3.10"', 'numpy>=1.23.2; python_version >= "3.11"']2 install pacakge
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!pip3.11 install siuba seaborn pandasql nbformat nbclient3 load package
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import pandas as pd
from pandasql import sqldf
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
from siuba.siu import call
from siuba import _, mutate, filter, group_by, summarize,show_query
from siuba import *
from siuba.data import mtcars,penguinsCode
mtcars=mtcarsCode
mtcars=r.mtcarsCode
mtcars.head(5) mpg cyl disp hp drat ... qsec vs am gear carb
Mazda RX4 21.0 6.0 160.0 110.0 3.90 ... 16.46 0.0 1.0 4.0 4.0
Mazda RX4 Wag 21.0 6.0 160.0 110.0 3.90 ... 17.02 0.0 1.0 4.0 4.0
Datsun 710 22.8 4.0 108.0 93.0 3.85 ... 18.61 1.0 1.0 4.0 1.0
Hornet 4 Drive 21.4 6.0 258.0 110.0 3.08 ... 19.44 1.0 0.0 3.0 1.0
Hornet Sportabout 18.7 8.0 360.0 175.0 3.15 ... 17.02 0.0 0.0 3.0 2.0
[5 rows x 11 columns]row name to column
Code
mtcars.index.name = 'newhead'
mtcars.reset_index(inplace=True)Code
mtcars.head(5) newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
2 Datsun 710 22.8 4.0 108.0 93.0 ... 18.61 1.0 1.0 4.0 1.0
3 Hornet 4 Drive 21.4 6.0 258.0 110.0 ... 19.44 1.0 0.0 3.0 1.0
4 Hornet Sportabout 18.7 8.0 360.0 175.0 ... 17.02 0.0 0.0 3.0 2.0
[5 rows x 12 columns]Code
small_mtcars = mtcars[["cyl", "mpg",'hp']]
small_mtcars=small_mtcars.head(5)
small_mtcars cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.03.1 get info on the data
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small_mtcars.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cyl 5 non-null float64
1 mpg 5 non-null float64
2 hp 5 non-null float64
dtypes: float64(3)
memory usage: 252.0 bytes4 select column
4.1 get column names
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list(small_mtcars)['cyl', 'mpg', 'hp']4.2 select by name
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small_mtcars [["cyl", "mpg",'hp']] cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.0other way:
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small_mtcars.filter(items=['cyl', 'mpg','hp']) cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.0Code
small_mtcars >> select(_.cyl, _.mpg, _.hp) cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.04.2.1 select columns by name match with ‘p’
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small_mtcars.loc[:,small_mtcars.columns.str.contains("p")] mpg hp
0 21.0 110.0
1 21.0 110.0
2 22.8 93.0
3 21.4 110.0
4 18.7 175.0other way:
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small_mtcars.filter(regex='p.*', axis=1) mpg hp
0 21.0 110.0
1 21.0 110.0
2 22.8 93.0
3 21.4 110.0
4 18.7 175.04.2.2 select columns by index
4.2.2.1 select first and 3rd columns
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small_mtcars.iloc[[0,2]] cyl mpg hp
0 6.0 21.0 110.0
2 4.0 22.8 93.04.2.2.2 select first to 3rd columns
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small_mtcars[0:3] cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.05 drop column
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small_mtcars.drop('cyl', axis=1) mpg hp
0 21.0 110.0
1 21.0 110.0
2 22.8 93.0
3 21.4 110.0
4 18.7 175.0Code
small_mtcars>>select(-_.cyl) mpg hp
0 21.0 110.0
1 21.0 110.0
2 22.8 93.0
3 21.4 110.0
4 18.7 175.06 Renaming column
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small_mtcars.rename(columns={'mpg':"new_name_mpg", 'cyl':'new_name_cyl'}) new_name_cyl new_name_mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.0Code
small_mtcars>> rename(new_name_mpg=_.mpg) cyl new_name_mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.07 Create column
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small_mtcars['mpg2'] = small_mtcars['mpg']+1
small_mtcars['mpg3'] = np.where(small_mtcars['mpg']> 20, "long", "short")
small_mtcars['mpg4'] =np.where(small_mtcars["mpg"]<19, "short",
np.where(small_mtcars["mpg"]<=22, "Medium",
np.where(small_mtcars["mpg"]>22, "long","else")))
small_mtcars cyl mpg hp mpg2 mpg3 mpg4
0 6.0 21.0 110.0 22.0 long Medium
1 6.0 21.0 110.0 22.0 long Medium
2 4.0 22.8 93.0 23.8 long long
3 6.0 21.4 110.0 22.4 long Medium
4 8.0 18.7 175.0 19.7 short shortCode
small_mtcars >> mutate(mpg2 = _.mpg+1
,mpg3=if_else(_.mpg > 20, "long", "short")
) cyl mpg hp mpg2 mpg3 mpg4
0 6.0 21.0 110.0 22.0 long Medium
1 6.0 21.0 110.0 22.0 long Medium
2 4.0 22.8 93.0 23.8 long long
3 6.0 21.4 110.0 22.4 long Medium
4 8.0 18.7 175.0 19.7 short short8 Transmute,create column and only keep this column
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small_mtcars['mpg2'] = small_mtcars['mpg']+1
new_data=small_mtcars[['mpg2']]
new_data mpg2
0 22.0
1 22.0
2 23.8
3 22.4
4 19.78.1 Filter rows
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mtcars[(mtcars['gear'] ==4)] newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
2 Datsun 710 22.8 4.0 108.0 93.0 ... 18.61 1.0 1.0 4.0 1.0
7 Merc 240D 24.4 4.0 146.7 62.0 ... 20.00 1.0 0.0 4.0 2.0
8 Merc 230 22.8 4.0 140.8 95.0 ... 22.90 1.0 0.0 4.0 2.0
9 Merc 280 19.2 6.0 167.6 123.0 ... 18.30 1.0 0.0 4.0 4.0
10 Merc 280C 17.8 6.0 167.6 123.0 ... 18.90 1.0 0.0 4.0 4.0
17 Fiat 128 32.4 4.0 78.7 66.0 ... 19.47 1.0 1.0 4.0 1.0
18 Honda Civic 30.4 4.0 75.7 52.0 ... 18.52 1.0 1.0 4.0 2.0
19 Toyota Corolla 33.9 4.0 71.1 65.0 ... 19.90 1.0 1.0 4.0 1.0
25 Fiat X1-9 27.3 4.0 79.0 66.0 ... 18.90 1.0 1.0 4.0 1.0
31 Volvo 142E 21.4 4.0 121.0 109.0 ... 18.60 1.0 1.0 4.0 2.0
[12 rows x 12 columns]other way: ::: {.cell}
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mtcars.query('gear==4') newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
2 Datsun 710 22.8 4.0 108.0 93.0 ... 18.61 1.0 1.0 4.0 1.0
7 Merc 240D 24.4 4.0 146.7 62.0 ... 20.00 1.0 0.0 4.0 2.0
8 Merc 230 22.8 4.0 140.8 95.0 ... 22.90 1.0 0.0 4.0 2.0
9 Merc 280 19.2 6.0 167.6 123.0 ... 18.30 1.0 0.0 4.0 4.0
10 Merc 280C 17.8 6.0 167.6 123.0 ... 18.90 1.0 0.0 4.0 4.0
17 Fiat 128 32.4 4.0 78.7 66.0 ... 19.47 1.0 1.0 4.0 1.0
18 Honda Civic 30.4 4.0 75.7 52.0 ... 18.52 1.0 1.0 4.0 2.0
19 Toyota Corolla 33.9 4.0 71.1 65.0 ... 19.90 1.0 1.0 4.0 1.0
25 Fiat X1-9 27.3 4.0 79.0 66.0 ... 18.90 1.0 1.0 4.0 1.0
31 Volvo 142E 21.4 4.0 121.0 109.0 ... 18.60 1.0 1.0 4.0 2.0
[12 rows x 12 columns]:::
8.1.1 Filters with AND conditions
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mtcars[(mtcars['cyl'] >4)&(mtcars['gear'] ==5) ] newhead mpg cyl disp hp ... qsec vs am gear carb
28 Ford Pantera L 15.8 8.0 351.0 264.0 ... 14.5 0.0 1.0 5.0 4.0
29 Ferrari Dino 19.7 6.0 145.0 175.0 ... 15.5 0.0 1.0 5.0 6.0
30 Maserati Bora 15.0 8.0 301.0 335.0 ... 14.6 0.0 1.0 5.0 8.0
[3 rows x 12 columns]other way: ::: {.cell}
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mtcars.query('cyl>4 and gear==5') newhead mpg cyl disp hp ... qsec vs am gear carb
28 Ford Pantera L 15.8 8.0 351.0 264.0 ... 14.5 0.0 1.0 5.0 4.0
29 Ferrari Dino 19.7 6.0 145.0 175.0 ... 15.5 0.0 1.0 5.0 6.0
30 Maserati Bora 15.0 8.0 301.0 335.0 ... 14.6 0.0 1.0 5.0 8.0
[3 rows x 12 columns]:::
8.1.2 Filters with OR conditions
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mtcars[(mtcars['cyl'] ==6) |(mtcars['gear'] ==5) ] newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
3 Hornet 4 Drive 21.4 6.0 258.0 110.0 ... 19.44 1.0 0.0 3.0 1.0
5 Valiant 18.1 6.0 225.0 105.0 ... 20.22 1.0 0.0 3.0 1.0
9 Merc 280 19.2 6.0 167.6 123.0 ... 18.30 1.0 0.0 4.0 4.0
10 Merc 280C 17.8 6.0 167.6 123.0 ... 18.90 1.0 0.0 4.0 4.0
26 Porsche 914-2 26.0 4.0 120.3 91.0 ... 16.70 0.0 1.0 5.0 2.0
27 Lotus Europa 30.4 4.0 95.1 113.0 ... 16.90 1.0 1.0 5.0 2.0
28 Ford Pantera L 15.8 8.0 351.0 264.0 ... 14.50 0.0 1.0 5.0 4.0
29 Ferrari Dino 19.7 6.0 145.0 175.0 ... 15.50 0.0 1.0 5.0 6.0
30 Maserati Bora 15.0 8.0 301.0 335.0 ... 14.60 0.0 1.0 5.0 8.0
[11 rows x 12 columns]other way: ::: {.cell}
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mtcars.query('cyl==6 or gear==5') newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
3 Hornet 4 Drive 21.4 6.0 258.0 110.0 ... 19.44 1.0 0.0 3.0 1.0
5 Valiant 18.1 6.0 225.0 105.0 ... 20.22 1.0 0.0 3.0 1.0
9 Merc 280 19.2 6.0 167.6 123.0 ... 18.30 1.0 0.0 4.0 4.0
10 Merc 280C 17.8 6.0 167.6 123.0 ... 18.90 1.0 0.0 4.0 4.0
26 Porsche 914-2 26.0 4.0 120.3 91.0 ... 16.70 0.0 1.0 5.0 2.0
27 Lotus Europa 30.4 4.0 95.1 113.0 ... 16.90 1.0 1.0 5.0 2.0
28 Ford Pantera L 15.8 8.0 351.0 264.0 ... 14.50 0.0 1.0 5.0 4.0
29 Ferrari Dino 19.7 6.0 145.0 175.0 ... 15.50 0.0 1.0 5.0 6.0
30 Maserati Bora 15.0 8.0 301.0 335.0 ... 14.60 0.0 1.0 5.0 8.0
[11 rows x 12 columns]:::
8.1.3 filter row with index
8.1.3.1 5th rows
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# not in siuba, in pandas
mtcars.iloc[[4]] newhead mpg cyl disp hp ... qsec vs am gear carb
4 Hornet Sportabout 18.7 8.0 360.0 175.0 ... 17.02 0.0 0.0 3.0 2.0
[1 rows x 12 columns]8.1.3.2 1 and 5tj rows
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# not in siuba, in pandas
mtcars.iloc[[0,4]] newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
4 Hornet Sportabout 18.7 8.0 360.0 175.0 ... 17.02 0.0 0.0 3.0 2.0
[2 rows x 12 columns]8.1.3.3 1 to 5th rows
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# not in siuba, in pandas
mtcars.iloc[0:4] newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
2 Datsun 710 22.8 4.0 108.0 93.0 ... 18.61 1.0 1.0 4.0 1.0
3 Hornet 4 Drive 21.4 6.0 258.0 110.0 ... 19.44 1.0 0.0 3.0 1.0
[4 rows x 12 columns]8.1.3.4 get ramdon 5 rows
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mtcars.sample(5, random_state=42) newhead mpg cyl disp hp ... qsec vs am gear carb
29 Ferrari Dino 19.7 6.0 145.0 175.0 ... 15.50 0.0 1.0 5.0 6.0
15 Lincoln Continental 10.4 8.0 460.0 215.0 ... 17.82 0.0 0.0 3.0 4.0
24 Pontiac Firebird 19.2 8.0 400.0 175.0 ... 17.05 0.0 0.0 3.0 2.0
17 Fiat 128 32.4 4.0 78.7 66.0 ... 19.47 1.0 1.0 4.0 1.0
8 Merc 230 22.8 4.0 140.8 95.0 ... 22.90 1.0 0.0 4.0 2.0
[5 rows x 12 columns]8.2 Append
8.2.1 append by row
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# not available in siuba yet
#from siuba import bind_rowsCode
# using pandas
# get 1 to 4 rows
data1=mtcars.iloc[0:4]
# get 9 rows
data2=mtcars.iloc[10:11]
data3=pd.concat([data1, data2], ignore_index = True,axis=0)
data3 newhead mpg cyl disp hp ... qsec vs am gear carb
0 Mazda RX4 21.0 6.0 160.0 110.0 ... 16.46 0.0 1.0 4.0 4.0
1 Mazda RX4 Wag 21.0 6.0 160.0 110.0 ... 17.02 0.0 1.0 4.0 4.0
2 Datsun 710 22.8 4.0 108.0 93.0 ... 18.61 1.0 1.0 4.0 1.0
3 Hornet 4 Drive 21.4 6.0 258.0 110.0 ... 19.44 1.0 0.0 3.0 1.0
4 Merc 280C 17.8 6.0 167.6 123.0 ... 18.90 1.0 0.0 4.0 4.0
[5 rows x 12 columns]8.2.2 append by column
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# using pandas
data1=small_mtcars[["cyl", "mpg"]]
data2=small_mtcars[['hp']]
data3=pd.concat([data1, data2], axis=1).reindex(data2.index)
data3 cyl mpg hp
0 6.0 21.0 110.0
1 6.0 21.0 110.0
2 4.0 22.8 93.0
3 6.0 21.4 110.0
4 8.0 18.7 175.08.2.3 Dropping NA values
8.2.4 keep NA values
8.3 group by
8.3.1 average,min,max,sum
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mtcars.groupby("cyl")["hp"].mean()cyl
4.0 82.636364
6.0 122.285714
8.0 209.214286
Name: hp, dtype: float64Code
mtcars.groupby("cyl")["hp"].min()cyl
4.0 52.0
6.0 105.0
8.0 150.0
Name: hp, dtype: float64Code
mtcars.groupby("cyl")["hp"].max()cyl
4.0 113.0
6.0 175.0
8.0 335.0
Name: hp, dtype: float64Code
mtcars.groupby("cyl")["hp"].sum()cyl
4.0 909.0
6.0 856.0
8.0 2929.0
Name: hp, dtype: float648.3.2 count record and count distinct record
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mtcars.groupby("cyl")["hp"].count()cyl
4.0 11
6.0 7
8.0 14
Name: hp, dtype: int64Code
mtcars.groupby("cyl")["hp"].nunique()cyl
4.0 10
6.0 4
8.0 9
Name: hp, dtype: int648.4 order rows
Code
small_mtcars.sort_values('hp') cyl mpg hp mpg2 mpg3 mpg4
2 4.0 22.8 93.0 23.8 long long
0 6.0 21.0 110.0 22.0 long Medium
1 6.0 21.0 110.0 22.0 long Medium
3 6.0 21.4 110.0 22.4 long Medium
4 8.0 18.7 175.0 19.7 short short8.4.1 Sort in descending order
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small_mtcars.sort_values('hp',ascending=False) cyl mpg hp mpg2 mpg3 mpg4
4 8.0 18.7 175.0 19.7 short short
0 6.0 21.0 110.0 22.0 long Medium
1 6.0 21.0 110.0 22.0 long Medium
3 6.0 21.4 110.0 22.4 long Medium
2 4.0 22.8 93.0 23.8 long long8.4.2 Arrange by multiple variables
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small_mtcars.sort_values(by=['cyl','mpg']) cyl mpg hp mpg2 mpg3 mpg4
2 4.0 22.8 93.0 23.8 long long
0 6.0 21.0 110.0 22.0 long Medium
1 6.0 21.0 110.0 22.0 long Medium
3 6.0 21.4 110.0 22.4 long Medium
4 8.0 18.7 175.0 19.7 short short8.5 join
Code
lhs = pd.DataFrame({'id': [1,2,3], 'val': ['lhs.1', 'lhs.2', 'lhs.3']})
rhs = pd.DataFrame({'id': [1,2,4], 'val': ['rhs.1', 'rhs.2', 'rhs.3']})Code
lhs id val
0 1 lhs.1
1 2 lhs.2
2 3 lhs.3Code
rhs id val
0 1 rhs.1
1 2 rhs.2
2 4 rhs.38.5.1 inner_join
Code
result=pd.merge(lhs, rhs, on='id', how='inner')
result id val_x val_y
0 1 lhs.1 rhs.1
1 2 lhs.2 rhs.28.5.2 full join
Code
result=pd.merge(lhs, rhs, on='id', how='outer')
result id val_x val_y
0 1 lhs.1 rhs.1
1 2 lhs.2 rhs.2
2 3 lhs.3 NaN
3 4 NaN rhs.38.5.3 left join
Code
result=pd.merge(lhs, rhs, on='id', how='left')
result id val_x val_y
0 1 lhs.1 rhs.1
1 2 lhs.2 rhs.2
2 3 lhs.3 NaN8.6 Reshape tables
Code
costs = pd.DataFrame({
'id': [1,2],
'price_x': [.1, .2],
'price_y': [.4, .5],
'price_z': [.7, .8]
})
costs id price_x price_y price_z
0 1 0.1 0.4 0.7
1 2 0.2 0.5 0.88.6.1 Gather data long(wide to long)
Code
# selecting each variable manually
long_date=pd.melt(costs,id_vars=['id'], value_vars=['price_x', 'price_y','price_z'])
long_date id variable value
0 1 price_x 0.1
1 2 price_x 0.2
2 1 price_y 0.4
3 2 price_y 0.5
4 1 price_z 0.7
5 2 price_z 0.8Code
#costs >> gather('measure', 'value', _.price_x, _.price_y, _.price_z)8.6.2 Spread data wide (long to wide)
Code
long_date.pivot(index="id", columns="variable", values="value")variable price_x price_y price_z
id
1 0.1 0.4 0.7
2 0.2 0.5 0.88.7 string
Code
df = pd.DataFrame({'text': ['abc', 'DDD','1243c','aeEe'], 'num': [3, 4,7,8]})
df text num
0 abc 3
1 DDD 4
2 1243c 7
3 aeEe 88.7.1 upper case
8.7.2 lower case
8.7.3 match
8.7.4 concatenation
8.7.5 replace
8.7.6 extract
Use str.extract() with a regular expression to pull out a matching piece of text.
For example the regular expression “^(.*) ” contains the following pieces:
a matches the literal letter “a”
.* has a . which matches anything, and * which modifies it to apply 0 or more times.
8.8 date
Code
df_dates = pd.DataFrame({
"dates": pd.to_datetime(["2021-01-02", "2021-02-03"]),
"raw": ["2023-04-05 06:07:08", "2024-05-06 07:08:09"],
})
df_dates dates raw
0 2021-01-02 2023-04-05 06:07:08
1 2021-02-03 2024-05-06 07:08:09Code
df_dates.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 dates 2 non-null datetime64[ns]
1 raw 2 non-null object
dtypes: datetime64[ns](1), object(1)
memory usage: 164.0+ bytes8.9 dataframe to numpy array
Code
df_dates.to_numpy()array([[Timestamp('2021-01-02 00:00:00'), '2023-04-05 06:07:08'],
[Timestamp('2021-02-03 00:00:00'), '2024-05-06 07:08:09']],
dtype=object)9 Reference:
https://pandas.pydata.org/docs/user_guide