4.19. DataFrame Join¶
import pandas as pd
import numpy as np
np.random.seed(0)
df1999 = pd.DataFrame(
columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
index = pd.date_range('1999-12-29', periods=3),
data = np.random.randn(3, 4))
df1999
# Morning Noon Evening Midnight
# 1999-12-29 1.764052 0.400157 0.978738 2.240893
# 1999-12-30 1.867558 -0.977278 0.950088 -0.151357
# 1999-12-31 -0.103219 0.410599 0.144044 1.454274
df2000 = pd.DataFrame(
columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
index = pd.date_range('2000-01-01', periods=3),
data = np.random.randn(3, 4))
# Morning Noon Evening Midnight
# 2000-01-01 0.761038 0.121675 0.443863 0.333674
# 2000-01-02 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-03 -2.552990 0.653619 0.864436 -0.742165
4.19.1. Concatenate¶
Useful for merging data from two files or datasources
pd.concat([df1999, df2000])
# Morning Noon Evening Midnight
# 1999-12-29 1.764052 0.400157 0.978738 2.240893
# 1999-12-30 1.867558 -0.977278 0.950088 -0.151357
# 1999-12-31 -0.103219 0.410599 0.144044 1.454274
# 2000-01-01 0.761038 0.121675 0.443863 0.333674
# 2000-01-02 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-03 -2.552990 0.653619 0.864436 -0.742165
4.19.2. Append¶
jak robi append, to nie zmienia indeksów (uwaga na indeksy powtórzone)
Resulting
DataFramewill have auto-incremented indexes
df1999.append(df2000)
# Morning Noon Evening Midnight
# 1999-12-29 1.764052 0.400157 0.978738 2.240893
# 1999-12-30 1.867558 -0.977278 0.950088 -0.151357
# 1999-12-31 -0.103219 0.410599 0.144044 1.454274
# 2000-01-01 0.761038 0.121675 0.443863 0.333674
# 2000-01-02 1.494079 -0.205158 0.313068 -0.854096
# 2000-01-03 -2.552990 0.653619 0.864436 -0.742165
df1999.append(df2000, ignore_index=True)
# Morning Noon Evening Midnight
# 0 1.764052 0.400157 0.978738 2.240893
# 1 1.867558 -0.977278 0.950088 -0.151357
# 2 -0.103219 0.410599 0.144044 1.454274
# 3 0.761038 0.121675 0.443863 0.333674
# 4 1.494079 -0.205158 0.313068 -0.854096
# 5 -2.552990 0.653619 0.864436 -0.742165
Add Row:
import pandas as pd
df = pd.DataFrame({
'A': [10, 11, 12],
'B': [20, 21, 22],
'C': [30, 31, 32]})
df
# A B C
# 0 10 20 30
# 1 11 21 31
# 2 12 22 32
df.append({'A': 77, 'B': 88, 'C': 99})
# Traceback (most recent call last):
# TypeError: Can only append a Series if ignore_index=True or if the Series has a name
df.append({'A': 77, 'B': 88, 'C': 99}, ignore_index=True)
# A B C
# 0 10 20 30
# 1 11 21 31
# 2 12 22 32
# 3 77 88 99
import pandas as pd
simple = pd.DataFrame({
'A': [10, 11, 12],
'B': [20, 21, 22],
'C': [30, 31, 32]})
new = pd.DataFrame([
{'A': 13, 'B': 23, 'C': 33},
{'A': 13, 'B': 23, 'C': 33},
{'A': 13, 'B': 23, 'C': 33},
])
simple.append(new)
simple.append(new, ignore_index=True)
import pandas as pd
import numpy as np
np.random.seed(0)
temp = pd.DataFrame(
columns = ['Morning', 'Noon', 'Evening', 'Midnight'],
index = pd.date_range('1999-12-30', periods=2),
data = np.random.randn(2, 4))
temp
# Morning Noon Evening Midnight
# 1999-12-30 1.532779 1.469359 0.154947 0.378163
# 1999-12-31 -0.887786 -1.980796 -0.347912 0.156349
new1 = pd.DataFrame([
{'Morning': 1, 'Noon': 2, 'Evening': 3, 'Midnight': 4}])
temp.append(new1)
# Morning Noon Evening Midnight
# 1999-12-30 1.532779 1.469359 0.154947 0.378163
# 1999-12-31 -0.887786 -1.980796 -0.347912 0.156349
# 0 1.000000 2.000000 3.000000 4.000000
temp.append(new1, ignore_index=True)
# Morning Noon Evening Midnight
# 1999-12-30 1.532779 1.469359 0.154947 0.378163
# 1999-12-31 -0.887786 -1.980796 -0.347912 0.156349
# 7 1.000000 2.000000 3.000000 4.000000
new2 = pd.DataFrame(
data = [{'Morning': 1, 'Noon': 2, 'Evening': 3, 'Midnight': 4}],
index= [pd.Timestamp('2000-01-01')])
temp.append(new2)
# Morning Noon Evening Midnight
# 1999-12-30 1.532779 1.469359 0.154947 0.378163
# 1999-12-31 -0.887786 -1.980796 -0.347912 0.156349
# 2000-01-01 1.000000 2.000000 3.000000 4.000000
temp.append(new2, ignore_index=True)
# Morning Noon Evening Midnight
# 1999-12-30 1.532779 1.469359 0.154947 0.378163
# 1999-12-31 -0.887786 -1.980796 -0.347912 0.156349
# 7 1.000000 2.000000 3.000000 4.000000
4.19.3. Merge¶
Merge DataFrame or named Series objects with a database-style join.
The join is done on columns or indexes.
If joining columns on columns, the DataFrame indexes will be ignored.
Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.
firstnames = pd.DataFrame({
'id': [1, 2, 3, 4],
'firstname': ['Mark', 'Jan', 'Ivan', 'Melissa']})
lastnames = pd.DataFrame({
'id': [1, 2, 3, 4],
'lastname': ['Watney', 'Twardowski', 'Ivanovic', 'Lewis']})
firstnames
# id firstname
# 0 1 Mark
# 1 2 Jan
# 2 3 Ivan
# 3 4 Melissa
lastnames
# id lastname
# 0 1 Watney
# 1 2 Twardowski
# 2 3 Ivanovic
# 3 4 Lewis
firstnames.merge(lastnames)
# id firstname lastname
# 0 1 Mark Watney
# 1 2 Jan Twardowski
# 2 3 Ivan Ivanovic
# 3 4 Melissa Lewis
firstnames.merge(lastnames, on='id')
# id firstname lastname
# 0 1 Mark Watney
# 1 2 Jan Twardowski
# 2 3 Ivan Ivanovic
# 3 4 Melissa Lewis
firstnames.merge(lastnames, left_on='id', right_on='id')
# id firstname lastname
# 0 1 Mark Watney
# 1 2 Jan Twardowski
# 2 3 Ivan Ivanovic
# 3 4 Melissa Lewis
firstnames.merge(lastnames).set_index('id')
# firstname lastname
# id
# 1 Mark Watney
# 2 Jan Twardowski
# 3 Ivan Ivanovic
# 4 Melissa Lewis
df1999.merge(df2000)
# Empty DataFrame
# Columns: [Morning, Noon, Evening, Midnight]
# Index: []
df1999.merge(df2000, right_index=True, left_index=True, how='left', suffixes=('_1999', '_2000'))
# Morning_1999 Noon_1999 ... Evening_2000 Midnight_2000
# 1999-12-29 1.764052 0.400157 ... NaN NaN
# 1999-12-30 1.867558 -0.977278 ... NaN NaN
# 1999-12-31 -0.103219 0.410599 ... NaN NaN
# [3 rows x 8 columns]
df1999.merge(df2000, how='outer')
# Morning Noon Evening Midnight
# 0 1.764052 0.400157 0.978738 2.240893
# 1 1.867558 -0.977278 0.950088 -0.151357
# 2 -0.103219 0.410599 0.144044 1.454274
# 3 0.761038 0.121675 0.443863 0.333674
# 4 1.494079 -0.205158 0.313068 -0.854096
# 5 -2.552990 0.653619 0.864436 -0.742165
4.19.4. Join¶
Join columns of another DataFrame.
Join columns with other DataFrame either on index or on a key column.
Efficiently join multiple DataFrame objects by index at once by passing a list.
rfuffix- If two columns has the same name, add suffix to rightlfuffix- If two columns has the same name, add suffix to left
Figure 4.15. Pandas DataFrame Joins¶
firstnames = pd.DataFrame({
'id': [1, 2, 3, 4],
'firstname': ['Mark', 'Jan', 'Ivan', 'Melissa']})
lastnames = pd.DataFrame({
'id': [1, 2, 3, 4],
'lastname': ['Watney', 'Twardowski', 'Ivanovic', 'Lewis']})
firstnames
# id firstname
# 0 1 Mark
# 1 2 Jan
# 2 3 Ivan
# 3 4 Melissa
lastnames
# id lastname
# 0 1 Watney
# 1 2 Twardowski
# 2 3 Ivanovic
# 3 4 Lewis
Join DataFrames using their indexes:
firstnames.join(lastnames, lsuffix='_fname', rsuffix='_lname')
# id_fname firstname id_lname lastname
# 0 1 Mark 1 Watney
# 1 2 Jan 2 Twardowski
# 2 3 Ivan 3 Ivanovic
# 3 4 Melissa 4 Lewis
firstnames.set_index('id').join(lastnames.set_index('id'))
# firstname lastname
# id
# 1 Mark Watney
# 2 Jan Twardowski
# 3 Ivan Ivanovic
# 4 Melissa Lewis
This method preserves the original DataFrame's index in the result:
firstnames.join(lastnames.set_index('id'), on='id')
# id firstname lastname
# 0 1 Mark Watney
# 1 2 Jan Twardowski
# 2 3 Ivan Ivanovic
# 3 4 Melissa Lewis
df1999.join(df2000, how='left', lsuffix='_1999', rsuffix='_2000')
# Morning_1999 Noon_1999 ... Evening_2000 Midnight_2000
# 1999-12-29 1.764052 0.400157 ... NaN NaN
# 1999-12-30 1.867558 -0.977278 ... NaN NaN
# 1999-12-31 -0.103219 0.410599 ... NaN NaN
# [3 rows x 8 columns]
df1999.join(df2000, how='outer', lsuffix='_1999', rsuffix='_2000')
# Morning_1999 Noon_1999 ... Evening_2000 Midnight_2000
# 1999-12-29 1.764052 0.400157 ... NaN NaN
# 1999-12-30 1.867558 -0.977278 ... NaN NaN
# 1999-12-31 -0.103219 0.410599 ... NaN NaN
# 2000-01-01 NaN NaN ... 0.443863 0.333674
# 2000-01-02 NaN NaN ... 0.313068 -0.854096
# 2000-01-03 NaN NaN ... 0.864436 -0.742165
# [6 rows x 8 columns]
4.19.5. Assignments¶
"""
* Assignment: DataFrame Join
* Complexity: medium
* Lines of code: 25 lines
* Time: 21 min
English:
TODO: Translate to English
Polish:
1. Na podstawie podanych URL:
a. https://www.worldspaceflight.com/bios/eva/eva.php
b. https://www.worldspaceflight.com/bios/eva/eva2.php
c. https://www.worldspaceflight.com/bios/eva/eva3.php
d. https://www.worldspaceflight.com/bios/eva/eva4.php
2. Scrapuj stronę wykorzystując `pandas.read_html()`
3. Połącz dane wykorzystując `pd.concat`
4. Przygotuj plik `CSV` z danymi dotyczącymi spacerów kosmicznych
5. Zapisz dane do pliku
Tests:
>>> pd.set_option('display.width', 500)
>>> pd.set_option('display.max_columns', 10)
>>> pd.set_option('display.max_rows', 10)
>>> result # doctest: +NORMALIZE_WHITESPACE
Ellipsis
TODO: Doctests
"""
# Given
import pandas as pd
result = ...