This mini project or whatever you call it as was done with the help of the website down below, which helped me in understanding, analysing the data by scraping the data from the internet.

Getting started with Data Analysis with Python Pandas

More importantly the "Titanic" dataset was retrived from Kaggle and the link can be found below:

Titanic Dataset

The following two lines of code must be entered in order to upload the file from the local drive, because google colab stores everything in your drive. So make sure that you that you read the article down below before you begin typing the code. Moreover, I dont want you guys to stuck and watch the screen when you get an error.

Click Here

In [3]:

from google.colab import files
uploaded = files.upload()

Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.

Saving train.csv to train.csv

After entering the above two lines of code, wait till you get the 100% uploaded confirmation. Finally once you get that, please enter the two more lines (I really don't know what the two line does)

In [0]:

import io
df2 = pd.read_csv(io.BytesIO(uploaded['train.csv']))

Importing the dataset with read_csv and displaying the data.¶

In [7]:

import pandas as pd
import csv
import matplotlib.pyplot as plt

df = pd.read_csv("train.csv")
df

Out[7]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
16	17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
18	19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C
20	21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0000	NaN	S
21	22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
22	23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
23	24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
25	26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38.0	1	5	347077	31.3875	NaN	S
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
28	29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
29	30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
861	862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
864	865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
866	867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
869	870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
870	871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
873	874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
875	876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
876	877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
877	878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
878	879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
881	882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
882	883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
883	884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
884	885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

Using Head and Tail methods inorder to display the data.¶

In [8]:

df.head(5)  # Used to display top 5

Out[8]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

In [9]:

df.tail(5) # Used to display last 5

Out[9]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.00	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.00	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.45	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.00	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.75	NaN	Q

Accessing specific coloums which are needed.¶

In [10]:

df = pd.read_csv("train.csv", usecols= ["PassengerId", "Survived", "Pclass", "Name", "Sex","Age"])
df.head()

Out[10]:

	PassengerId	Survived	Pclass	Name	Sex	Age
0	1	0	3	Braund, Mr. Owen Harris	male	22.0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0
4	5	0	3	Allen, Mr. William Henry	male	35.0

Get more information about the database by using describe method.¶

In [11]:

df.describe()

Out[11]:

	PassengerId	Survived	Pclass	Age
count	891.000000	891.000000	891.000000	714.000000
mean	446.000000	0.383838	2.308642	29.699118
std	257.353842	0.486592	0.836071	14.526497
min	1.000000	0.000000	1.000000	0.420000
25%	223.500000	0.000000	2.000000	20.125000
50%	446.000000	0.000000	3.000000	28.000000
75%	668.500000	1.000000	3.000000	38.000000
max	891.000000	1.000000	3.000000	80.000000

Sorting the values by using the sort method.¶

In [12]:

df.sort_values("Age")
df.head()

Out[12]:

	PassengerId	Survived	Pclass	Name	Sex	Age
0	1	0	3	Braund, Mr. Owen Harris	male	22.0
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0
2	3	1	3	Heikkinen, Miss. Laina	female	26.0
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0
4	5	0	3	Allen, Mr. William Henry	male	35.0

Sorting the values according to the age.¶

In [13]:

df = df.sort_values("Age", ascending = False)
df.head(5)

Out[13]:

	PassengerId	Survived	Pclass	Name	Sex	Age
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0
851	852	0	3	Svensson, Mr. Johan	male	74.0
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0
96	97	0	1	Goldschmidt, Mr. George B	male	71.0
116	117	0	3	Connors, Mr. Patrick	male	70.5

Filtering the dataset, or cleaning the dataset by selecting only the name and many more.¶

In [14]:

result = df[df['Name'] ==  'Svensson, Mr. Johan'	]
result

Out[14]:

	PassengerId	Survived	Pclass	Name	Sex	Age
851	852	0	3	Svensson, Mr. Johan	male	74.0

Counting the occurences of variables¶

In [15]:

df["Sex"].value_counts()

Out[15]:

male      577
female    314
Name: Sex, dtype: int64

Using .nunique() to count number of unique values that occur in dataset or in a column¶

In [16]:

df.nunique()

Out[16]:

PassengerId    891
Survived         2
Pclass           3
Name           891
Sex              2
Age             88
dtype: int64

Filtering¶

AND operator

In [17]:

df_age = df["Age"] < 50
df_sex_mask = df["Sex"] == "female"
df[df_age & df_sex_mask]

Out[17]:

	PassengerId	Survived	Pclass	Name	Sex	Age
52	53	1	1	Harper, Mrs. Henry Sleeper (Myna Haxtun)	female	49.00
796	797	1	1	Leader, Dr. Alice (Farnham)	female	49.00
754	755	1	2	Herman, Mrs. Samuel (Jane Laver)	female	48.00
556	557	1	1	Duff Gordon, Lady. (Lucille Christiana Sutherl...	female	48.00
736	737	0	3	Ford, Mrs. Edward (Margaret Ann Watson)	female	48.00
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.00
132	133	0	3	Robins, Mrs. Alexander A (Grace Charity Laury)	female	47.00
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.00
706	707	1	2	Kelly, Mrs. Florence "Fannie"	female	45.00
276	277	0	3	Lindblom, Miss. Augusta Charlotta	female	45.00
167	168	0	3	Skoog, Mrs. William (Anna Bernhardina Karlsson)	female	45.00
362	363	0	3	Barbara, Mrs. (Catherine David)	female	45.00
856	857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.00
440	441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	female	45.00
523	524	1	1	Hippach, Mrs. Louis Albert (Ida Sophia Fischer)	female	44.00
194	195	1	1	Brown, Mrs. James Joseph (Margaret Tobin)	female	44.00
854	855	0	2	Carter, Mrs. Ernest Courtenay (Lilian Hughes)	female	44.00
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton Mc...	female	43.00
678	679	0	3	Goodwin, Mrs. Frederick (Augusta Tyler)	female	43.00
432	433	1	2	Louch, Mrs. Charles Alexander (Alice Adelaide ...	female	42.00
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.00
380	381	1	1	Bidois, Miss. Rosalie	female	42.00
272	273	1	2	Mellinger, Mrs. (Elizabeth Anne Maidment)	female	41.00
337	338	1	1	Burns, Miss. Elizabeth Margaret	female	41.00
254	255	0	3	Rosblom, Mrs. Viktor (Helena Wilhelmina)	female	41.00
638	639	0	3	Panula, Mrs. Juha (Maria Emilia Ojala)	female	41.00
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.00
319	320	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corn...	female	40.00
161	162	1	2	Watt, Mrs. James (Elizabeth "Bessie" Inglis Mi...	female	40.00
346	347	1	2	Smith, Miss. Marion Elsie	female	40.00
...	...	...	...	...	...	...
634	635	0	3	Skoog, Miss. Mabel	female	9.00
852	853	0	3	Boulos, Miss. Nourelain	female	9.00
541	542	0	3	Andersson, Miss. Ingeborg Constanzia	female	9.00
147	148	0	3	Ford, Miss. Robina Maggie "Ruby"	female	9.00
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.00
237	238	1	2	Collyer, Miss. Marjorie "Lottie"	female	8.00
535	536	1	2	Hart, Miss. Eva Miriam	female	7.00
720	721	1	2	Harper, Miss. Annie Jessie "Nina"	female	6.00
813	814	0	3	Andersson, Miss. Ebba Iris Alfrida	female	6.00
777	778	1	3	Emanuel, Miss. Virginia Ethel	female	5.00
233	234	1	3	Asplund, Miss. Lillian Gertrud	female	5.00
58	59	1	2	West, Miss. Constance Mirium	female	5.00
448	449	1	3	Baclini, Miss. Marie Catherine	female	5.00
691	692	1	3	Karun, Miss. Manca	female	4.00
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.00
750	751	1	2	Wells, Miss. Joan	female	4.00
184	185	1	3	Kink-Heilmann, Miss. Luise Gretchen	female	4.00
618	619	1	2	Becker, Miss. Marion Louise	female	4.00
43	44	1	2	Laroche, Miss. Simonne Marie Anne Andree	female	3.00
374	375	0	3	Palsson, Miss. Stina Viola	female	3.00
642	643	0	3	Skoog, Miss. Margit Elizabeth	female	2.00
205	206	0	3	Strom, Miss. Telma Matilda	female	2.00
530	531	1	2	Quick, Miss. Phyllis May	female	2.00
479	480	1	3	Hirvonen, Miss. Hildur E	female	2.00
297	298	0	1	Allison, Miss. Helen Loraine	female	2.00
119	120	0	3	Andersson, Miss. Ellis Anna Maria	female	2.00
381	382	1	3	Nakid, Miss. Maria ("Mary")	female	1.00
172	173	1	3	Johnson, Miss. Eleanor Ileen	female	1.00
644	645	1	3	Baclini, Miss. Eugenie	female	0.75
469	470	1	3	Baclini, Miss. Helene Barbara	female	0.75

239 rows × 6 columns

OR operator

In [18]:

df_sex = df["Sex"] == "Male"
df_age_mask = df["Age"] > 70
df[df_sex | df_age_mask]

Out[18]:

	PassengerId	Survived	Pclass	Name	Sex	Age
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0
851	852	0	3	Svensson, Mr. Johan	male	74.0
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0
96	97	0	1	Goldschmidt, Mr. George B	male	71.0
116	117	0	3	Connors, Mr. Patrick	male	70.5

Finding the null values with .isnull()¶

In [19]:

null_mask = df["Age"].isnull()
df[null_mask]

Out[19]:

	PassengerId	Survived	Pclass	Name	Sex	Age
5	6	0	3	Moran, Mr. James	male	NaN
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN
28	29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN
29	30	0	3	Todoroff, Mr. Lalio	male	NaN
31	32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN
32	33	1	3	Glynn, Miss. Mary Agatha	female	NaN
36	37	1	3	Mamee, Mr. Hanna	male	NaN
42	43	0	3	Kraeff, Mr. Theodor	male	NaN
45	46	0	3	Rogers, Mr. William John	male	NaN
46	47	0	3	Lennon, Mr. Denis	male	NaN
47	48	1	3	O'Driscoll, Miss. Bridget	female	NaN
48	49	0	3	Samaan, Mr. Youssef	male	NaN
55	56	1	1	Woolner, Mr. Hugh	male	NaN
64	65	0	1	Stewart, Mr. Albert A	male	NaN
65	66	1	3	Moubarek, Master. Gerios	male	NaN
76	77	0	3	Staneff, Mr. Ivan	male	NaN
77	78	0	3	Moutal, Mr. Rahamin Haim	male	NaN
82	83	1	3	McDermott, Miss. Brigdet Delia	female	NaN
87	88	0	3	Slocovski, Mr. Selman Francis	male	NaN
95	96	0	3	Shorney, Mr. Charles Joseph	male	NaN
101	102	0	3	Petroff, Mr. Pastcho ("Pentcho")	male	NaN
107	108	1	3	Moss, Mr. Albert Johan	male	NaN
109	110	1	3	Moran, Miss. Bertha	female	NaN
121	122	0	3	Moore, Mr. Leonard Charles	male	NaN
126	127	0	3	McMahon, Mr. Martin	male	NaN
128	129	1	3	Peter, Miss. Anna	female	NaN
140	141	0	3	Boulos, Mrs. Joseph (Sultana)	female	NaN
154	155	0	3	Olsen, Mr. Ole Martin	male	NaN
...	...	...	...	...	...	...
718	719	0	3	McEvoy, Mr. Michael	male	NaN
727	728	1	3	Mannion, Miss. Margareth	female	NaN
732	733	0	2	Knight, Mr. Robert J	male	NaN
738	739	0	3	Ivanoff, Mr. Kanio	male	NaN
739	740	0	3	Nankoff, Mr. Minko	male	NaN
740	741	1	1	Hawksford, Mr. Walter James	male	NaN
760	761	0	3	Garfirth, Mr. John	male	NaN
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN
768	769	0	3	Moran, Mr. Daniel J	male	NaN
773	774	0	3	Elias, Mr. Dibo	male	NaN
776	777	0	3	Tobin, Mr. Roger	male	NaN
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN
783	784	0	3	Johnston, Mr. Andrew G	male	NaN
790	791	0	3	Keane, Mr. Andrew "Andy"	male	NaN
792	793	0	3	Sage, Miss. Stella Anna	female	NaN
793	794	0	1	Hoyt, Mr. William Fisher	male	NaN
815	816	0	1	Fry, Mr. Richard	male	NaN
825	826	0	3	Flynn, Mr. John	male	NaN
826	827	0	3	Lam, Mr. Len	male	NaN
828	829	1	3	McCormack, Mr. Thomas Joseph	male	NaN
832	833	0	3	Saad, Mr. Amin	male	NaN
837	838	0	3	Sirota, Mr. Maurice	male	NaN
839	840	1	1	Marechal, Mr. Pierre	male	NaN
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN
849	850	1	1	Goldenberg, Mrs. Samuel L (Edwiga Grabowska)	female	NaN
859	860	0	3	Razi, Mr. Raihed	male	NaN
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN
878	879	0	3	Laleff, Mr. Kristo	male	NaN
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN

177 rows × 6 columns

In [20]:

df.isnull().sum()

Out[20]:

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
dtype: int64

Dropping a column¶

In [21]:

df.drop(labels = ["Pclass"], axis=1).head()

Out[21]:

	PassengerId	Survived	Name	Sex	Age
630	631	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0
851	852	0	Svensson, Mr. Johan	male	74.0
493	494	0	Artagaveytia, Mr. Ramon	male	71.0
96	97	0	Goldschmidt, Mr. George B	male	71.0
116	117	0	Connors, Mr. Patrick	male	70.5

Replacing the values by using the replace method

In [22]:

df.replace("Nan",df["Age"].median())

df.replace("Masselmani, Mrs. Fatima", "Tanu")

Out[22]:

	PassengerId	Survived	Pclass	Name	Sex	Age
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0
851	852	0	3	Svensson, Mr. Johan	male	74.0
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0
96	97	0	1	Goldschmidt, Mr. George B	male	71.0
116	117	0	3	Connors, Mr. Patrick	male	70.5
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0
33	34	0	2	Wheadon, Mr. Edward H	male	66.0
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0
280	281	0	3	Duane, Mr. Frank	male	65.0
456	457	0	1	Millet, Mr. Francis Davis	male	65.0
438	439	0	1	Fortune, Mr. Mark	male	64.0
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.0
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0
570	571	1	2	Harris, Mr. George	male	62.0
252	253	0	1	Stead, Mr. William Thomas	male	62.0
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0
555	556	0	1	Wright, Mr. George	male	62.0
625	626	0	1	Sutton, Mr. Frederick	male	61.0
326	327	0	3	Nysveen, Mr. Johan Hansen	male	61.0
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0
684	685	0	2	Brown, Mr. Thomas William Solomon	male	60.0
694	695	0	1	Weir, Col. John	male	60.0
587	588	1	1	Frolicher-Stehli, Mr. Maxmillian	male	60.0
366	367	1	1	Warren, Mrs. Frank Manley (Anna Sophia Atkinson)	female	60.0
94	95	0	3	Coxon, Mr. Daniel	male	59.0
232	233	0	2	Sjostedt, Mr. Ernst Adolf	male	59.0
268	269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.0
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0
...	...	...	...	...	...	...
718	719	0	3	McEvoy, Mr. Michael	male	NaN
727	728	1	3	Mannion, Miss. Margareth	female	NaN
732	733	0	2	Knight, Mr. Robert J	male	NaN
738	739	0	3	Ivanoff, Mr. Kanio	male	NaN
739	740	0	3	Nankoff, Mr. Minko	male	NaN
740	741	1	1	Hawksford, Mr. Walter James	male	NaN
760	761	0	3	Garfirth, Mr. John	male	NaN
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN
768	769	0	3	Moran, Mr. Daniel J	male	NaN
773	774	0	3	Elias, Mr. Dibo	male	NaN
776	777	0	3	Tobin, Mr. Roger	male	NaN
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN
783	784	0	3	Johnston, Mr. Andrew G	male	NaN
790	791	0	3	Keane, Mr. Andrew "Andy"	male	NaN
792	793	0	3	Sage, Miss. Stella Anna	female	NaN
793	794	0	1	Hoyt, Mr. William Fisher	male	NaN
815	816	0	1	Fry, Mr. Richard	male	NaN
825	826	0	3	Flynn, Mr. John	male	NaN
826	827	0	3	Lam, Mr. Len	male	NaN
828	829	1	3	McCormack, Mr. Thomas Joseph	male	NaN
832	833	0	3	Saad, Mr. Amin	male	NaN
837	838	0	3	Sirota, Mr. Maurice	male	NaN
839	840	1	1	Marechal, Mr. Pierre	male	NaN
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN
849	850	1	1	Goldenberg, Mrs. Samuel L (Edwiga Grabowska)	female	NaN
859	860	0	3	Razi, Mr. Raihed	male	NaN
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN
878	879	0	3	Laleff, Mr. Kristo	male	NaN
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN

891 rows × 6 columns

Let us calculate how many passengers survived.¶

Here 1 = survived, and 0 = Not survived.

In [41]:

count = df['Survived'].value_counts()
print(count)
# Let us see that in percentage.

percentage = df['Survived'].value_counts() * 100 / len(df)
print(percentage)

0    549
1    342
Name: Survived, dtype: int64
0    61.616162
1    38.383838
Name: Survived, dtype: float64

In [42]:

%matplotlib inline
color = 0.5
df['Survived'].value_counts().plot(kind = 'bar')

Out[42]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f0fcd0e0710>

I think this much more than enough for a good start to just starting and master data analysis on the web. Further, I will add more concepts, snippets, and examples in class to make things clear

Click Here to watch the video