Introduction To Pandas

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.

What is Pandas ?

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.

In more simpler terms , pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.

Installing Pandas

Let's Install Pandas by using pip package manager :

1. Open your terminal in Pycharm

2. Write : pip install pandas

Read CSV and Text files Through Pandas

In [ ]:

import pandas as pd
df = pd.read_csv('Pokemon.csv')
print(df)

Result:

Read Top few rows or Bottom few:

head() Function :

The head() function is used to get the first or last n rows.This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

In [ ]:

print(df.head(5))    #Top 5 Rows

print(df.head(-5))    #Last 5 Rows

Load Data Without Header

In [ ]:

head=["#","Name","Type 1","Type 2","HP","Attack","Defense","Sp. Atk","Sp. Def","Speed","Generation","Legendary"]

df = pd.read_csv(r"Pokemon.csv",names=head)

print(df)

To Read Data Separated by any delimiter :

In [ ]:

df = pd.read_csv('Pokemon.csv', delimiter=',')

print(df.head(5))

To work on Specific Column:

Say , We want to work on HP Column.

In [ ]:

col = df['HP']
print(col)

To work on more than one columns:

In [ ]:

cols = df[['Name', 'Type 1', 'HP']]
print(cols)

To Read Headers:

In [ ]:

df.columns

To get the Data Types Of Columns:

To get the data types of columns , you need to use dtype attribute.

In [ ]:

df.dtypes

Statistical Describing Data

The Describe function returns the statistical summary of the dataframe or series. This includes Count, Mean, Median (or 50th percentile) standard variation, min-max, and percentile values of columns.

In [19]:

df.describe()

#	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation
count	800.000000	800.00000	800.000000	800.000000	800.000000	800.000000	800.000000	800.000000	800.00000
mean	362.813750	435.10250	69.258750	79.001250	73.842500	72.820000	71.902500	68.277500	3.32375
std	208.343798	119.96304	25.534669	32.457366	31.183501	32.722294	27.828916	29.060474	1.66129
min	1.000000	180.00000	1.000000	5.000000	5.000000	10.000000	20.000000	5.000000	1.00000
25%	184.750000	330.00000	50.000000	55.000000	50.000000	49.750000	50.000000	45.000000	2.00000
50%	364.500000	450.00000	65.000000	75.000000	70.000000	65.000000	70.000000	65.000000	3.00000
75%	539.250000	515.00000	80.000000	100.000000	90.000000	95.000000	90.000000	90.000000	5.00000
max	721.000000	780.00000	255.000000	190.000000	230.000000	194.000000	230.000000	180.000000	6.00000

Slicing Pandas Data frame using DataFrame.iloc[]

We can perform slicing on columns as well as rows on pandas Dataframe.

Syntax For Slicing:

df.iloc[rows,columns]

Slicing For Rows :

Say We want to slice Top 4 rows with all columns , We will slice it in the same way as we learned it in Numpy.

df.iloc[0:4,:]

#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False

Slicing For Columns :

Say We want to slice 4 Columns from start with all rows , We will do it something like this.

In [21]:

df.iloc[:,0:4]

#	Name	Type 1	Type 2
0	1	Bulbasaur	Grass	Poison
1	2	Ivysaur	Grass	Poison
2	3	Venusaur	Grass	Poison
3	3	VenusaurMega Venusaur	Grass	Poison
4	4	Charmander	Fire	NaN
...	...	...	...	...
795	719	Diancie	Rock	Fairy
796	719	DiancieMega Diancie	Rock	Fairy
797	720	HoopaHoopa Confined	Psychic	Ghost
798	720	HoopaHoopa Unbound	Psychic	Dark
799	721	Volcanion	Fire	Water

800 rows × 4 columns

Performing Iteration In DataFrame :

In this topic, we will learn to iterate over :

1.Rows 2.Columns

Iteration Over Rows Using iterrows() Function :

You can iterate over rows with the help of iterrows().It gives you a tuple of index and whole row. Say i want index and only name from the row, We will write it something like this:

In [ ]:

for index, row in df.iterrows():
    print(index, row['Name'])

Iteration Over Columns Using iteritems() Function :

In [25]:

for (colname,colval) in df.iteritems():
    print(colname)

#
Name
Type 1
Type 2
Total
HP
Attack
Defense
Sp. Atk
Sp. Def
Speed
Generation
Legendary

Filtering Data in Pandas DataFrame Using loc[] :

Selecting all the rows from the given Dataframe in which Type1 is Grass using loc[ ].

In [26]:

df.loc[df['Type 1'] == "Grass"]

#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
48	43	Oddish	Grass	Poison	320	45	50	55	75	65	30	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
718	650	Chespin	Grass	NaN	313	56	61	65	48	45	38	6	False
719	651	Quilladin	Grass	NaN	405	61	78	95	56	58	57	6	False
720	652	Chesnaught	Grass	Fighting	530	88	107	122	74	75	64	6	False
740	672	Skiddo	Grass	NaN	350	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	531	123	100	62	97	81	68	6	False

70 rows × 13 columns

Sorting of Data in DataFrame Using sort.values() Function :

Sort in Ascending Order

You can use sort.values() function to sort data in ascending or descending order.For Example, We want to sort Type1 in Ascending Order.

In [35]:

df.sort_values(['Type 1'])

Out[35]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
600	540	Sewaddle	Bug	Grass	310	45	53	70	40	60	42	5	False
136	127	Pinsir	Bug	NaN	500	65	125	100	55	70	85	1	False
457	412	Burmy	Bug	NaN	224	40	29	45	29	45	36	4	False
132	123	Scyther	Bug	Flying	500	70	110	80	55	80	105	1	False
656	595	Joltik	Bug	Electric	319	50	47	50	57	50	65	5	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
172	158	Totodile	Water	NaN	314	50	65	64	44	48	43	2	False
610	550	Basculin	Water	NaN	460	70	92	65	80	55	98	5	False
145	134	Vaporeon	Water	NaN	525	130	65	60	110	95	65	1	False
574	515	Panpour	Water	NaN	316	50	53	48	53	48	64	5	False
184	170	Chinchou	Water	Electric	330	75	38	38	56	56	67	2	False

800 rows × 13 columns

Sort in Descending Order

By using ascending=False parameter, You can Sort Data in Descending Order.

In [36]:

df.sort_values(['Type 1'],ascending=False)

Out[36]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
371	339	Barboach	Water	Ground	288	50	48	43	46	41	60	3	False
97	90	Shellder	Water	NaN	305	30	65	100	45	25	40	1	False
240	222	Corsola	Water	Rock	380	55	55	85	65	85	35	2	False
403	368	Gorebyss	Water	NaN	485	55	84	105	114	75	52	3	False
93	86	Seel	Water	NaN	325	65	45	55	45	70	45	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
54	49	Venomoth	Bug	Poison	450	70	65	60	90	75	90	1	False
208	193	Yanma	Bug	Flying	390	65	65	45	75	45	95	2	False
182	168	Ariados	Bug	Poison	390	70	90	70	60	60	40	2	False
734	666	Vivillon	Bug	Flying	411	80	52	50	90	50	89	6	False
179	165	Ledyba	Bug	Flying	265	40	20	30	40	80	55	2	False

800 rows × 13 columns

Sorting of Multiple Columns in different order using sort.values():

In [37]:

df.sort_values(['Type 1','Type 2','Total','HP'],ascending=[True,False,True,False])

Out[37]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
307	283	Surskit	Bug	Water	269	40	30	32	50	52	65	3	False
460	413	WormadamTrash Cloak	Bug	Steel	424	60	69	95	69	95	36	4	False
220	205	Forretress	Bug	Steel	465	75	90	140	60	60	40	2	False
693	632	Durant	Bug	Steel	484	58	109	112	48	48	109	5	False
650	589	Escavalier	Bug	Steel	495	70	135	105	60	105	20	5	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
264	245	Suicune	Water	NaN	580	100	75	115	90	115	85	2	True
548	490	Manaphy	Water	NaN	600	100	100	100	100	100	100	4	False
12	9	BlastoiseMega Blastoise	Water	NaN	630	79	103	120	135	115	78	1	False
421	382	Kyogre	Water	NaN	670	100	100	90	150	140	90	3	True
422	382	KyogrePrimal Kyogre	Water	NaN	770	100	150	90	180	160	90	3	True

800 rows × 13 columns

---

As you can see, We have sorted multiple columns in different order. Type 1 and Total is in Ascending order , while Type 2 and HP is in Descending Order.

You can also use 1 and 0 instead of True and False.

df.sort_values(['Type 1','Type 2','Total','HP'],ascending=[1,0,1,0])

Result will be same.

---

How to Create New Column in DataFrame Using Old Ones:

Say , We want to create a new Column named total_sum , that is addition of HP,Attack,Defense,Sp.atk,Sp.def,Speed,Generation.

In [44]:

df['total_sum'] = df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']
df['total_sum']

Out[44]:

0      273
1      345
2      445
3      545
4      270
      ... 
795    550
796    650
797    520
798    600
799    520
Name: total_sum, Length: 800, dtype: int64

We can also use sum() function to achieve same column , for Example :

In [55]:

df['total_s'] = df.iloc[:,6:11].sum(axis=1)
df['total_s']

Out[55]:

0      273
1      345
2      445
3      545
4      270
      ... 
795    550
796    650
797    520
798    600
799    520
Name: total_s, Length: 800, dtype: int64

---

NOTE

If i See my dataframe, it will add both columns. Let's see.

In [56]:

df

Out[56]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	T	total_sum	total_s
0	1	Bulbasaur	Grass	Poison	273	45	49	49	65	65	45	1	False	273	273	273
1	2	Ivysaur	Grass	Poison	345	60	62	63	80	80	60	1	False	345	345	345
2	3	Venusaur	Grass	Poison	445	80	82	83	100	100	80	1	False	445	445	445
3	3	VenusaurMega Venusaur	Grass	Poison	545	80	100	123	122	120	80	1	False	545	545	545
4	4	Charmander	Fire	NaN	270	39	52	43	60	50	65	1	False	270	270	270
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	550	50	100	150	100	150	50	6	True	550	550	550
796	719	DiancieMega Diancie	Rock	Fairy	650	50	160	110	160	110	110	6	True	650	650	650
797	720	HoopaHoopa Confined	Psychic	Ghost	520	80	110	60	150	130	70	6	True	520	520	520
798	720	HoopaHoopa Unbound	Psychic	Dark	600	80	160	60	170	130	80	6	True	600	600	600
799	721	Volcanion	Fire	Water	520	80	110	120	130	90	70	6	True	520	520	520

800 rows × 16 columns

How to change Columns Positions :

In [59]:

df=df[['#','Legendary', 'Generation', 'Speed', 'Sp.Def', 'Sp.Atk', 'Defense', 'Attack',
       'HP', 'Type 2', 'Type 1', 'Name']]
df

Out[59]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

800 rows × 12 columns

In [ ]:

cols = df.columns
df = df[cols[0:4]+cols[4:12]]
df

Exporting Data And Creating a New File :

To Export File in CSV:

Let's say we have a data player_score.txt and we want to convert it into learn_pandas.csv. Here is our data player_score.txt in text form.

import pandas as pd
df = pd.read_csv('player_score.txt')
df.to_csv('player_score.txt')
df

Out[2]:

	#	Player	Match1	Match2	Match3
0	1	Virat	100	25	75
1	2	Dhoni	78	93	25
2	3	Jadeja	56	78	90
3	4	Ashwin	78	47	112
4	5	Sachin	112	45	78

If you do not want index in your file, you can simply put index=False

---

Parameters :

index : Lets you insert index by changing index=True and index=False.

sep : lets you select sep , for example sep=',' or sep='@'.

---

Say, You want no index Column and separator should be ',' :

In [4]:

df.to_csv('player_score.csv',index=False,sep=',')
df

Out[4]:

	#	Player	Match1	Match2	Match3
0	1	Virat	100	25	75
1	2	Dhoni	78	93	25
2	3	Jadeja	56	78	90
3	4	Ashwin	78	47	112
4	5	Sachin	112	45	78

To Export File in Excel:

In [7]:

df.to_excel('player_score.xlsx')
df

Out[7]:

	#	Player	Match1	Match2	Match3
0	1	Virat	100	25	75
1	2	Dhoni	78	93	25
2	3	Jadeja	56	78	90
3	4	Ashwin	78	47	112
4	5	Sachin	112	45	78

Deleting a Column by using drop() :

DataFrame has a method called drop() that removes rows or columns according to specify column(label) names and corresponding axis.

Parameters :

1.inplace=True means the operation would work on the original object.

2.axis = 1 we are dropping the column, not the row.

In [12]:

import pandas as pd
df = pd.read_csv('Pokemon.csv')
#Deleting a Column Generation
new_df = df.drop(columns=['Total'])
new_df

Out[12]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

800 rows × 12 columns

Reset all the indexes :

If the original index are numbers, now we have indexes that are not continuous. Well, pandas has reset_index() function. So to reset the index to the default integer index beginning at 0, We can simply use the reset_index() function.

In [13]:

new_df.reset_index(drop=True,inplace=True)
new_df

Out[13]:

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

800 rows × 12 columns

Filtering Data with Multiple Conditions using loc[]:

In [14]:

filtered_df = df.loc[((df['Type 1'] == 'Grass') | (df['Type 2'] == 'Poison')) & (df['HP'] > 70)]
filtered_df

Out[14]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
50	45	Vileplume	Grass	Poison	490	75	80	85	110	90	50	1	False
77	71	Victreebel	Grass	Poison	490	80	105	65	100	70	70	1	False
79	73	Tentacruel	Water	Poison	515	80	70	65	80	120	100	1	False
111	103	Exeggutor	Grass	Psychic	520	95	95	85	125	65	55	1	False
168	154	Meganium	Grass	NaN	525	80	82	100	83	100	80	2	False
197	182	Bellossom	Grass	NaN	490	75	80	95	90	100	50	2	False
204	189	Jumpluff	Grass	Flying	460	75	55	70	55	95	110	2	False
207	192	Sunflora	Grass	NaN	425	75	75	55	105	85	30	2	False
298	275	Shiftry	Grass	Dark	480	90	100	60	90	60	80	3	False
390	357	Tropius	Grass	Flying	460	99	68	83	72	87	51	3	False
433	388	Grotle	Grass	NaN	405	75	89	85	55	65	36	4	False
434	389	Torterra	Grass	Ground	525	95	109	105	75	85	56	4	False
505	455	Carnivine	Grass	NaN	454	74	100	72	90	72	46	4	False
510	460	Abomasnow	Grass	Ice	494	90	92	75	92	85	60	4	False
511	460	AbomasnowMega Abomasnow	Grass	Ice	594	90	132	105	132	105	30	4	False
516	465	Tangrowth	Grass	NaN	535	100	100	125	110	50	50	4	False
550	492	ShayminLand Forme	Grass	NaN	600	100	100	100	100	100	100	4	True
551	492	ShayminSky Forme	Grass	Flying	600	100	103	75	120	75	127	4	True
556	497	Serperior	Grass	NaN	528	75	75	95	75	95	113	5	False
571	512	Simisage	Grass	NaN	498	75	98	63	98	63	101	5	False
617	556	Maractus	Grass	NaN	461	75	86	67	106	67	60	5	False
652	591	Amoonguss	Grass	Poison	464	114	85	70	85	80	30	5	False
659	598	Ferrothorn	Grass	Steel	489	74	94	131	54	116	20	5	False
701	640	Virizion	Grass	Fighting	580	91	90	72	90	129	108	5	True
720	652	Chesnaught	Grass	Fighting	530	88	107	122	74	75	64	6	False
741	673	Gogoat	Grass	NaN	531	123	100	62	97	81	68	6	False

To Replace Values in Column Based on Condition in Pandas :

In [16]:

df.loc[df['Total'] > 500, 'Generation'] = 5
df

Out[16]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	5	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	5	False
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	600	50	100	150	100	150	50	5	True
796	719	DiancieMega Diancie	Rock	Fairy	700	50	160	110	160	110	110	5	True
797	720	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	150	130	70	5	True
798	720	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	170	130	80	5	True
799	721	Volcanion	Fire	Water	600	80	110	120	130	90	70	5	True

800 rows × 13 columns

Aggregate Statistics (Groupby) perform grouping operations:

Pandas GroupBy is a powerful and versatile function in Python. It allows you to split your data into separate groups to perform computations for better analysis.

Let me take an example to elaborate on this. Let’s say we are trying to analyze the weight of a person in a city. We can easily get a fair idea of their weight by determining the mean weight of all the city dwellers. But here ‘s a question – would the weight be affected by the gender of a person?

We can group the city dwellers into different gender groups and calculate their mean weight. This would give us a better insight into the weight of a person living in the city. But we can probably get an even better picture if we further separate these gender groups into different age groups and then take their mean weight (because a teenage boy’s weight could differ from that of an adult male)!

In [11]:

df.groupby(['Type 1', 'Type 2']).count()['count']

Out[11]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	count
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False	1
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False	1
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False	1
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False	1
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	600	50	100	150	100	150	50	6	True	1
796	719	DiancieMega Diancie	Rock	Fairy	700	50	160	110	160	110	110	6	True	1
797	720	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	150	130	70	6	True	1
798	720	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	170	130	80	6	True	1
799	721	Volcanion	Fire	Water	600	80	110	120	130	90	70	6	True	1

800 rows × 14 columns

Initialize All Values of A Column :

Say We want to initialize all values of column count as 1.

In [10]:

df['count']=1
df

Out[10]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	count
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False	1
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False	1
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False	1
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False	1
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	600	50	100	150	100	150	50	6	True	1
796	719	DiancieMega Diancie	Rock	Fairy	700	50	160	110	160	110	110	6	True	1
797	720	HoopaHoopa Confined	Psychic	Ghost	600	80	110	60	150	130	70	6	True	1
798	720	HoopaHoopa Unbound	Psychic	Dark	680	80	160	60	170	130	80	6	True	1
799	721	Volcanion	Fire	Water	600	80	110	120	130	90	70	6	True	1

800 rows × 14 columns

Working With Large Amount of Data :

In [14]:

new_df = pd.DataFrame(columns=df.columns)

for df in pd.read_csv('Pokemon.csv', chunksize=5):
    results = df.groupby(['Type 1']).count()
    
    new_df = pd.concat([new_df, results])

new_df

Out[14]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
Fire	1	1	NaN	0	1	1	1	1	1	1	1	1	1
Grass	4	4	NaN	4	4	4	4	4	4	4	4	4	4
Fire	4	4	NaN	3	4	4	4	4	4	4	4	4	4
Water	1	1	NaN	0	1	1	1	1	1	1	1	1	1
Bug	2	2	NaN	0	2	2	2	2	2	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Fairy	1	1	NaN	0	1	1	1	1	1	1	1	1	1
Flying	2	2	NaN	2	2	2	2	2	2	2	2	2	2
Fire	1	1	NaN	1	1	1	1	1	1	1	1	1	1
Psychic	2	2	NaN	2	2	2	2	2	2	2	2	2	2
Rock	2	2	NaN	2	2	2	2	2	2	2	2	2	2

433 rows × 13 columns

Generate a Column With Random Numbers :

You can generate a column with random numbers by using random module . For Example , here total column is random number column.

In [18]:

import random
df['Total']=df['Total'].apply(lambda x: random.randint(1,100))
df

Out[18]:

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	54	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	69	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	17	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	34	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	18	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	49	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	80	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	43	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	7	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	67	80	110	120	130	90	70	6	True

800 rows × 13 columns