pandas练习

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#机器学习 

import pandas as pd 
print(pd.__version__) # 检查版本，如果太低请在终端使用 conda update pandas 命令进行升级

1.0.3

?pd.read_csv

df = pd.read_csv("./NBAPlayers.txt",sep = '\t') #以\t 缩进为分割

df.head()
Playerheightweightcollagebornbirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
df['Player']

0         Curly Armstrong
1            Cliff Barker
2           Leo Barnhorst
3              Ed Bartels
4             Ralph Beard
              ...        
3917        Troy Williams
3918         Kyle Wiltjer
3919    Stephen Zimmerman
3920          Paul Zipser
3921          Ivica Zubac
Name: Player, Length: 3922, dtype: object

df.iloc[:,0]

0         Curly Armstrong
1            Cliff Barker
2           Leo Barnhorst
3              Ed Bartels
4             Ralph Beard
              ...        
3917        Troy Williams
3918         Kyle Wiltjer
3919    Stephen Zimmerman
3920          Paul Zipser
3921          Ivica Zubac
Name: Player, Length: 3922, dtype: object

df.loc[:,'Player']

0         Curly Armstrong
1            Cliff Barker
2           Leo Barnhorst
3              Ed Bartels
4             Ralph Beard
              ...        
3917        Troy Williams
3918         Kyle Wiltjer
3919    Stephen Zimmerman
3920          Paul Zipser
3921          Ivica Zubac
Name: Player, Length: 3922, dtype: object

a = {"name":"xiaoming","age":18,"sex":"male"}

pd.Series(a)

name    xiaoming
age           18
sex         male
dtype: object

[*enumerate(a)]

[(0, 'name'), (1, 'age'), (2, 'sex')]

b = [1,2,3,4,5,6]

s1 = pd.Series(b,index=list('abcdef'))
s1

a    1
b    2
c    3
d    4
e    5
f    6
dtype: int64

s1.values

array([1, 2, 3, 4, 5, 6], dtype=int64)

s1.value_counts()

6    1
5    1
4    1
3    1
2    1
1    1
dtype: int64

b = [[1,2,3,4],['a','b','c','d']]

pd.DataFrame(b)
0123
01234
1abcd
pd.DataFrame(b, index = list("EF"),columns = list('ABCD')) #行名,列名 都是传入list,列表
ABCD
E1234
Fabcd
a = {"name":"xiaoming","age":18,"sex":"male"}

pd.DataFrame(a,index=list('AB')) #字典索引列名
nameagesex
Axiaoming18male
Bxiaoming18male
df.head()
Playerheightweightcollagebornbirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
df.rename(columns={'height':'Height','collage':'Collage','birth_city':'Birth_city'},inplace=True)

df.head()
PlayerHeightweightCollagebornBirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
df.sort_values(by =['Collage','Height'])
PlayerHeightweightCollagebornBirth_citybirth_state
803Brian Heaney188.081.0Acadia University1946.0NaNNaN
3468Mickell Gladness211.099.0Alabama A&M University1986.0BirminghamAlabama
1501Kevin Loder198.092.0Alabama State University1959.0CassopolisMichigan
1368Major Jones206.0102.0Albany State University1953.0McGheeArkansas
1613Charles Jones206.097.0Albany State University1957.0McGeheeArkansas
........................
3018Peter John226.0117.0NaN1985.0NaNNaN
2249P.J. Brown229.0106.0NaN1972.0NaNNaN
2878Yao Ming*229.0140.0NaN1980.0ShanghaiChina
2297Gheorghe Muresan231.0137.0NaN1971.0TriteniRomania
223NaNNaNNaNNaNNaNNaNNaN

3922 rows × 7 columns

df.replace({'Player':{'Curly Armstrong':'xiaozhao'}}) #replace 替换名字 可以用字典套字典套嵌
PlayerHeightweightCollagebornBirth_citybirth_state
0xiaozhao180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
........................
3917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
3920Paul Zipser203.097.0NaN1994.0HeidelbergGermany
3921Ivica Zubac216.0120.0NaN1997.0MostarBosnia and Herzegovina

3922 rows × 7 columns

df.min()

Height     160.0
weight      60.0
born      1913.0
dtype: float64

import numpy as np

pd.value_counts(df['born'])

1970.0    84
1964.0    77
1955.0    76
1968.0    74
1984.0    73
          ..
1917.0     6
1918.0     5
1915.0     2
1914.0     1
1913.0     1
Name: born, Length: 84, dtype: int64

df.sum(axis = 0)

Height     779122.0
weight     371645.0
born      7694491.0
dtype: float64

a = np.array([[1,2,3,4,5,56],[3,4,5,1,7,3],[29,3,1,6,2,0]])
a.sum()

135

a.sum(axis=1)

array([71, 23, 41])

a.sum(axis=0)

array([33,  9,  9, 11, 14, 59])

pd.isnull(df['Player'])

0       False
1       False
2       False
3       False
4       False
        ...  
3917    False
3918    False
3919    False
3920    False
3921    False
Name: Player, Length: 3922, dtype: bool

df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
PlayerHeightweightCollagebornBirth_citybirth_state
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
6Charlie Black196.090.0University of Kansas1921.0ArcoIdaho
7Nelson Bobb183.077.0Temple University1924.0PhiladelphiaPennsylvania
8Jake Bornheimer196.090.0Muhlenberg College1927.0New BrunswickNew Jersey
........................
3915Okaro White203.092.0Florida State University1992.0ClearwaterFlorida
3916Isaiah Whitehead193.096.0Seton Hall University1995.0BrooklynNew York
3917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee

3189 rows × 7 columns

df.fillna(value=0, method=None, axis=None, inplace=False, limit=None, downcast=None)
PlayerHeightweightCollagebornBirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.000
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.000
3Ed Bartels196.088.0North Carolina State University1925.000
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
........................
3917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
3920Paul Zipser203.097.001994.0HeidelbergGermany
3921Ivica Zubac216.0120.001997.0MostarBosnia and Herzegovina

3922 rows × 7 columns

s = pd.Series(['A', 'B', 'C', 'Aaba ', ' Baca', 'CABA ', 'dog', 'cat'])

s.str.strip()

0       A
1       B
2       C
3    Aaba
4    Baca
5    CABA
6     dog
7     cat
dtype: object

s.str.upper()

0        A
1        B
2        C
3    AABA 
4     BACA
5    CABA 
6      DOG
7      CAT
dtype: object

s[s.str.strip().str.endswith("a")]

3    Aaba 
4     Baca
dtype: object

df.loc[df['Height']>=180]
PlayerHeightweightCollagebornBirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
5Gene Berce180.079.0Marquette University1926.0NaNNaN
........................
3917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
3920Paul Zipser203.097.0NaN1994.0HeidelbergGermany
3921Ivica Zubac216.0120.0NaN1997.0MostarBosnia and Herzegovina

3869 rows × 7 columns

df.loc[:,['Player','Collage','Birth_city']]
PlayerCollageBirth_city
0Curly ArmstrongIndiana UniversityNaN
1Cliff BarkerUniversity of KentuckyYorktown
2Leo BarnhorstUniversity of Notre DameNaN
3Ed BartelsNorth Carolina State UniversityNaN
4Ralph BeardUniversity of KentuckyHardinsburg
............
3917Troy WilliamsSouth Carolina State UniversityColumbia
3918Kyle WiltjerGonzaga UniversityPortland
3919Stephen ZimmermanUniversity of Nevada, Las VegasHendersonville
3920Paul ZipserNaNHeidelberg
3921Ivica ZubacNaNMostar

3922 rows × 3 columns

df.loc[(df['Height']>=180) & (df['weight']>=80)]
PlayerHeightweightCollagebornBirth_citybirth_state
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
6Charlie Black196.090.0University of Kansas1921.0ArcoIdaho
8Jake Bornheimer196.090.0Muhlenberg College1927.0New BrunswickNew Jersey
........................
3917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
3920Paul Zipser203.097.0NaN1994.0HeidelbergGermany
3921Ivica Zubac216.0120.0NaN1997.0MostarBosnia and Herzegovina

3543 rows × 7 columns

df = pd.read_csv("movie.csv")
df.head()
colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenres...num_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
0ColorJames Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-Fi...3054.0EnglishUSAPG-13237000000.02009.0936.07.91.7833000
1ColorGore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|Fantasy...1238.0EnglishUSAPG-13300000000.02007.05000.07.12.350
2ColorSam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|Thriller...994.0EnglishUKPG-13245000000.02015.0393.06.82.3585000
3ColorChristopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|Thriller...2701.0EnglishUSAPG-13250000000.02012.023000.08.52.35164000
4NaNDoug WalkerNaNNaN131.0NaNRob Walker131.0NaNDocumentary...NaNNaNNaNNaNNaNNaN12.07.1NaN0

5 rows × 28 columns

df.head()
colordirector_namenum_critic_for_reviewsdurationdirector_facebook_likesactor_3_facebook_likesactor_2_nameactor_1_facebook_likesgrossgenres...num_user_for_reviewslanguagecountrycontent_ratingbudgettitle_yearactor_2_facebook_likesimdb_scoreaspect_ratiomovie_facebook_likes
0ColorJames Cameron723.0178.00.0855.0Joel David Moore1000.0760505847.0Action|Adventure|Fantasy|Sci-Fi...3054.0EnglishUSAPG-13237000000.02009.0936.07.91.7833000
1ColorGore Verbinski302.0169.0563.01000.0Orlando Bloom40000.0309404152.0Action|Adventure|Fantasy...1238.0EnglishUSAPG-13300000000.02007.05000.07.12.350
2ColorSam Mendes602.0148.00.0161.0Rory Kinnear11000.0200074175.0Action|Adventure|Thriller...994.0EnglishUKPG-13245000000.02015.0393.06.82.3585000
3ColorChristopher Nolan813.0164.022000.023000.0Christian Bale27000.0448130642.0Action|Thriller...2701.0EnglishUSAPG-13250000000.02012.023000.08.52.35164000
4NaNDoug WalkerNaNNaN131.0NaNRob Walker131.0NaNDocumentary...NaNNaNNaNNaNNaNNaN12.07.1NaN0

5 rows × 28 columns

df = pd.read_csv("./NBAPlayers.txt",sep = '\t') #以\t 缩进为分割

df.head()
Playerheightweightcollagebornbirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
new_df = df.set_index(keys=['birth_city','birth_state'],append=True,drop = False)

new_df.head()
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
0NaNNaNCurly Armstrong180.077.0Indiana University1918.0NaNNaN
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2NaNNaNLeo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3NaNNaNEd Bartels196.088.0North Carolina State University1925.0NaNNaN
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
new_df.sort_index(na_position="last",inplace=True)

new_df
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
0NaNNaNCurly Armstrong180.077.0Indiana University1918.0NaNNaN
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2NaNNaNLeo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3NaNNaNEd Bartels196.088.0North Carolina State University1925.0NaNNaN
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
..............................
3917ColumbiaSouth CarolinaTroy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
3918PortlandOregonKyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
3919HendersonvilleTennesseeStephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
3920HeidelbergGermanyPaul Zipser203.097.0NaN1994.0HeidelbergGermany
3921MostarBosnia and HerzegovinaIvica Zubac216.0120.0NaN1997.0MostarBosnia and Herzegovina

3922 rows × 7 columns

new_df.loc[(slice(None),['Akron','Ahvaz','Albany'],slice(None)),:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
71AlbanyTexasChick Halbert206.0102.0West Texas A&M University1919.0AlbanyTexas
539AkronOhioJimmy Darrow178.077.0Bowling Green State University1937.0AkronOhio
609AkronOhioGus Johnson*198.0104.0University of Idaho1938.0AkronOhio
620AkronOhioNate Thurmond*211.0102.0Bowling Green State University1941.0AkronOhio
1028AlbanyGeorgiaBen Clyde201.089.0Florida State University1951.0AlbanyGeorgia
1927AkronOhioJerome Lane198.0104.0University of Pittsburgh1966.0AkronOhio
2374AlbanyGeorgiaDontonio Wingfield203.0116.0University of Cincinnati1974.0AlbanyGeorgia
2756AlbanyGeorgiaLavor Postell196.097.0St. John's University1978.0AlbanyGeorgia
2882AkronOhioChris Owens201.0111.0University of Texas at Austin1979.0AkronOhio
2944AkronOhioLeBron James203.0113.0NaN1984.0AkronOhio
2985AlbanyNew YorkLionel Chalmers183.081.0Xavier University1980.0AlbanyNew York
3163AlbanyGeorgiaAlexander Johnson206.0108.0Florida State University1983.0AlbanyGeorgia
3287AhvazIslamic Republic of IranHamed Haddadi218.0115.0NaN1985.0AhvazIslamic Republic of Iran
3343AkronOhioStephen Curry190.086.0Davidson College1988.0AkronOhio
new_df.loc[idx[0:500,:],:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
0NaNNaNCurly Armstrong180.077.0Indiana University1918.0NaNNaN
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2NaNNaNLeo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3NaNNaNEd Bartels196.088.0North Carolina State University1925.0NaNNaN
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
..............................
496DaytonOhioJohnny Green196.090.0Michigan State University1933.0DaytonOhio
497DuryeaPennsylvaniaGene Guarilia196.099.0George Washington University1937.0DuryeaPennsylvania
498ChicagoIllinoisTom Hawkins196.095.0University of Notre Dame1936.0ChicagoIllinois
499MiddletonTennesseeBailey Howell*201.095.0Mississippi State University1937.0MiddletonTennessee
500NaNNaNMaury King188.088.0University of Kansas1935.0NaNNaN

501 rows × 7 columns

new_df_1 = df.loc[:,['birth_city',"birth_state",'collage']]

new_df_1
birth_citybirth_statecollage
0NaNNaNIndiana University
1YorktownIndianaUniversity of Kentucky
2NaNNaNUniversity of Notre Dame
3NaNNaNNorth Carolina State University
4HardinsburgKentuckyUniversity of Kentucky
............
3917ColumbiaSouth CarolinaSouth Carolina State University
3918PortlandOregonGonzaga University
3919HendersonvilleTennesseeUniversity of Nevada, Las Vegas
3920HeidelbergGermanyNaN
3921MostarBosnia and HerzegovinaNaN

3922 rows × 3 columns

df_1 = df.set_index(keys=['birth_city','birth_state'],append=True,drop = False)

df_1.loc[idx[0:200,["Yorktown","Hardinsburg"]],:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
154YorktownIndianaJack Parkinson183.078.0University of Kentucky1924.0YorktownIndiana
df_2 = df_1.loc[idx[0:200,["Yorktown","Hardinsburg"]],:]

df_2
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
154YorktownIndianaJack Parkinson183.078.0University of Kentucky1924.0YorktownIndiana
df_3 = df_2.loc[:,['Player','collage']]

df_1 = df.set_index(keys=['birth_city','birth_state'],append=True,drop = False)

new_df.loc[idx[0:500,['Yorktown'],[]],:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
new_df.loc[idx[0:500,['Yorktown'],[]],:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
1YorktownIndianaCliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
154YorktownIndianaJack Parkinson183.078.0University of Kentucky1924.0YorktownIndiana
# IndexSlice是一种更接近自然语法的用法，可以替换slice
idx = pd.IndexSlice

new_df.loc[idx[0:20,['Brooklyn'],['Ohio','New York']],:idx["Player"]]
Player
birth_citybirth_state
11BrooklynNew YorkHarry Boykoff
14BrooklynNew YorkCarl Braun
new_df.loc[idx[0:200,['Hardinsburg']],:]
Playerheightweightcollagebornbirth_citybirth_state
birth_citybirth_state
4HardinsburgKentuckyRalph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
df[0:21]
np.concatenate()
Playerheightweightcollagebornbirth_citybirth_state
0Curly Armstrong180.077.0Indiana University1918.0NaNNaN
1Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
2Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
3Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
4Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
5Gene Berce180.079.0Marquette University1926.0NaNNaN
6Charlie Black196.090.0University of Kansas1921.0ArcoIdaho
7Nelson Bobb183.077.0Temple University1924.0PhiladelphiaPennsylvania
8Jake Bornheimer196.090.0Muhlenberg College1927.0New BrunswickNew Jersey
9Vince Boryla196.095.0University of Denver1927.0East ChicagoIndiana
10Don Boven193.095.0Western Michigan University1925.0KalamazooMichigan
11Harry Boykoff208.0102.0St. John's University1922.0BrooklynNew York
12Joe Bradley190.079.0Oklahoma State University1928.0WashingtonOklahoma
13Bob Brannum196.097.0Michigan State University1925.0NaNNaN
14Carl Braun196.081.0Colgate University1927.0BrooklynNew York
15Frankie Brian185.081.0Louisiana State University1923.0ZacharyLouisiana
16Price Brookfield193.083.0West Texas A&M University1920.0FloydadaTexas
17Bob Brown193.092.0Miami University1923.0VersaillesOhio
18Jim Browne208.0106.0NaN1930.0MidlothianIllinois
19Walt Budko196.099.0Columbia University1925.0KearneyNew Jersey
20Jack Burmaster190.086.0University of Illinois at Urbana-Champaign1926.0NaNNaN
pd.read_csv("./NBAPlayers.txt",sep='\t',header=None)
0123456
0Playerheightweightcollagebornbirth_citybirth_state
1Curly Armstrong18077Indiana University1918NaNNaN
2Cliff Barker18883University of Kentucky1921YorktownIndiana
3Leo Barnhorst19386University of Notre Dame1924NaNNaN
4Ed Bartels19688North Carolina State University1925NaNNaN
........................
3918Troy Williams19897South Carolina State University1969ColumbiaSouth Carolina
3919Kyle Wiltjer208108Gonzaga University1992PortlandOregon
3920Stephen Zimmerman213108University of Nevada, Las Vegas1996HendersonvilleTennessee
3921Paul Zipser20397NaN1994HeidelbergGermany
3922Ivica Zubac216120NaN1997MostarBosnia and Herzegovina

3923 rows × 7 columns

df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']})

drop_t = df.set_index('A',drop=True, append=False, inplace=False, verify_integrity=False)

drop_t
BCD
A
A0B0C0D0
A1B1C1D1
A2B2C2D2
A3B3C3D3
no_drop_t = df.set_index('A',drop=False, append=False, inplace=False, verify_integrity=False)

no_drop_t
ABCD
A
A0A0B0C0D0
A1A1B1C1D1
A2A2B2C2D2
A3A3B3C3D3
reset_drop_t = drop_t.reset_index(drop=False)

reset_drop_t
ABCD
0A0B0C0D0
1A1B1C1D1
2A2B2C2D2
3A3B3C3D3
df_1 = df.reset_index(drop=False)

df_1
indexPlayerheightweightcollagebornbirth_citybirth_state
00Curly Armstrong180.077.0Indiana University1918.0NaNNaN
11Cliff Barker188.083.0University of Kentucky1921.0YorktownIndiana
22Leo Barnhorst193.086.0University of Notre Dame1924.0NaNNaN
33Ed Bartels196.088.0North Carolina State University1925.0NaNNaN
44Ralph Beard178.079.0University of Kentucky1927.0HardinsburgKentucky
...........................
39173917Troy Williams198.097.0South Carolina State University1969.0ColumbiaSouth Carolina
39183918Kyle Wiltjer208.0108.0Gonzaga University1992.0PortlandOregon
39193919Stephen Zimmerman213.0108.0University of Nevada, Las Vegas1996.0HendersonvilleTennessee
39203920Paul Zipser203.097.0NaN1994.0HeidelbergGermany
39213921Ivica Zubac216.0120.0NaN1997.0MostarBosnia and Herzegovina

3922 rows × 8 columns
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