Datawhale组队学习动手学数据分析第一章

1.1载入数据

任务1：导入numpy和pandas

import numpy as np
import pandas as pd
import os

任务二：载入数据

(1) 使用相对路径载入

cwd = os.getcwd()
os.chdir("D:\datasets\Titanic")
df = pd.read_csv('train.csv')
df.head()

	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

(2) 使用绝对路径载入数据

df = pd.read_csv('D:\\datasets\\Titanic\\train.csv')
df.head()

	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

任务三：每1000行为一个数据模块，逐块读取

chunker = pd.read_csv('train.csv', chunksize=1000)
chunker

任务四：将表头改成中文，索引改为乘客ID

df = pd.read_csv('train.csv', names=['乘客ID','是否幸存','仓位等级','姓名','性别','年龄','兄弟姐妹个数','父母子女个数','船票信息','票价','客舱','登船港口'],index_col='乘客ID',header=0)
df.head()

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

1.2初步观察

任务一：查看数据的基本信息

df.info

df.head(10)

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

df.tail(15)

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

任务三：判断数据是否为空，为空的地方返回True，其余地方返回False

df.isnull()

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
1	False	False	False	False	False	False	False	False	False	True	False
2	False	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	True	False
4	False	False	False	False	False	False	False	False	False	False	False
5	False	False	False	False	False	False	False	False	False	True	False
...	...	...	...	...	...	...	...	...	...	...	...
887	False	False	False	False	False	False	False	False	False	True	False
888	False	False	False	False	False	False	False	False	False	False	False
889	False	False	False	False	True	False	False	False	False	True	False
890	False	False	False	False	False	False	False	False	False	False	False
891	False	False	False	False	False	False	False	False	False	True	False

891 rows × 11 columns

1.3 保存数据

任务一：将你加载并做出改变的数据，在工作目录下保存为一个新文件train_chinese.csv

df.to_csv('train.chinese.csv')

2.1知道你的数据叫什么

任务一：pandas中有两个数据类型DateFrame和Series，通过查找简单了解他们。然后自己写一个关于这两个数据类型的小例子

sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
example_1 = pd.Series(sdata)
example_1

Ohio      35000
Texas     71000
Oregon    16000
Utah       5000
dtype: int64

data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002, 2003],'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
example_2 = pd.DataFrame(data)
example_2

	state	year	pop
0	Ohio	2000	1.5
1	Ohio	2001	1.7
2	Ohio	2002	3.6
3	Nevada	2001	2.4
4	Nevada	2002	2.9
5	Nevada	2003	3.2

任务二：根据上节课的方法载入"train.csv"文件

df=pd.read_csv('train.chinese.csv')
df.head()

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
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

任务三：查看DataFrame数据的每列的项

df.columns

Index(['乘客ID', '是否幸存', '仓位等级', '姓名', '性别', '年龄', '兄弟姐妹个数', '父母子女个数', '船票信息',
       '票价', '客舱', '登船港口'],
      dtype='object')

任务四：查看"cabin"这列的所有项

dir(df['客舱'])

['T',
 '_AXIS_ALIASES',
 '_AXIS_IALIASES',
 '_AXIS_LEN',
 '_AXIS_NAMES',
 '_AXIS_NUMBERS',
 '_AXIS_ORDERS',
 '_AXIS_REVERSED',
 '_HANDLED_TYPES',
 '__abs__',
 '__add__',
 '__and__',
 '__annotations__',
 '__array__',
 '__array_priority__',
 '__array_ufunc__',
 '__array_wrap__',
 '__bool__',
 '__class__',
 '__contains__',
 '__copy__',
 '__deepcopy__',
 '__delattr__',
 '__delitem__',
 '__dict__',
 '__dir__',
 '__div__',
 '__divmod__',
 '__doc__',
 '__eq__',
 '__finalize__',
 '__float__',
 '__floordiv__',
 '__format__',
 '__ge__',
 '__getattr__',
 '__getattribute__',
 '__getitem__',
 '__getstate__',
 '__gt__',
 '__hash__',
 '__iadd__',
 '__iand__',
 '__ifloordiv__',
 '__imod__',
 '__imul__',
 '__init__',
 '__init_subclass__',
 '__int__',
 '__invert__',
 '__ior__',
 '__ipow__',
 '__isub__',
 '__iter__',
 '__itruediv__',
 '__ixor__',
 '__le__',
 '__len__',
 '__long__',
 '__lt__',
 '__matmul__',
 '__mod__',
 '__module__',
 '__mul__',
 '__ne__',
 '__neg__',
 '__new__',
 '__nonzero__',
 '__or__',
 '__pos__',
 '__pow__',
 '__radd__',
 '__rand__',
 '__rdiv__',
 '__rdivmod__',
 '__reduce__',
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 '__ror__',
 '__round__',
 '__rpow__',
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 '__rxor__',
 '__setattr__',
 '__setitem__',
 '__setstate__',
 '__sizeof__',
 '__str__',
 '__sub__',
 '__subclasshook__',
 '__truediv__',
 '__weakref__',
 '__xor__',
 '_accessors',
 '_add_numeric_operations',
 '_add_series_or_dataframe_operations',
 '_agg_by_level',
 '_agg_examples_doc',
 '_agg_see_also_doc',
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 '_aggregate_multiple_funcs',
 '_align_frame',
 '_align_series',
 '_binop',
 '_box_item_values',
 '_builtin_table',
 '_can_hold_na',
 '_check_inplace_setting',
 '_check_is_chained_assignment_possible',
 '_check_label_or_level_ambiguity',
 '_check_setitem_copy',
 '_clear_item_cache',
 '_clip_with_one_bound',
 '_clip_with_scalar',
 '_consolidate',
 '_consolidate_inplace',
 '_construct_axes_dict',
 '_construct_axes_dict_from',
 '_construct_axes_from_arguments',
 '_constructor',
 '_constructor_expanddim',
 '_constructor_sliced',
 '_convert',
 '_convert_dtypes',
 '_create_indexer',
 '_cython_table',
 '_deprecations',
 '_dir_additions',
 '_dir_deletions',
 '_drop_axis',
 '_drop_labels_or_levels',
 '_find_valid_index',
 '_from_axes',
 '_get_axis',
 '_get_axis_name',
 '_get_axis_number',
 '_get_axis_resolvers',
 '_get_block_manager_axis',
 '_get_bool_data',
 '_get_cacher',
 '_get_cleaned_column_resolvers',
 '_get_cython_func',
 '_get_index_resolvers',
 '_get_item_cache',
 '_get_label_or_level_values',
 '_get_numeric_data',
 '_get_value',
 '_get_values',
 '_get_values_tuple',
 '_get_with',
 '_gotitem',
 '_iget_item_cache',
 '_index',
 '_indexed_same',
 '_info_axis',
 '_info_axis_name',
 '_info_axis_number',
 '_init_dict',
 '_init_mgr',
 '_internal_get_values',
 '_internal_names',
 '_internal_names_set',
 '_is_builtin_func',
 '_is_cached',
 '_is_copy',
 '_is_datelike_mixed_type',
 '_is_label_or_level_reference',
 '_is_label_reference',
 '_is_level_reference',
 '_is_mixed_type',
 '_is_numeric_mixed_type',
 '_is_view',
 '_ix',
 '_ixs',
 '_map_values',
 '_maybe_cache_changed',
 '_maybe_update_cacher',
 '_metadata',
 '_ndarray_values',
 '_needs_reindex_multi',
 '_obj_with_exclusions',
 '_protect_consolidate',
 '_reduce',
 '_reindex_axes',
 '_reindex_indexer',
 '_reindex_multi',
 '_reindex_with_indexers',
 '_repr_data_resource_',
 '_repr_latex_',
 '_reset_cache',
 '_reset_cacher',
 '_selected_obj',
 '_selection',
 '_selection_list',
 '_selection_name',
 '_set_as_cached',
 '_set_axis',
 '_set_axis_name',
 '_set_is_copy',
 '_set_item',
 '_set_labels',
 '_set_name',
 '_set_subtyp',
 '_set_value',
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 '_set_with',
 '_set_with_engine',
 '_setup_axes',
 '_slice',
 '_stat_axis',
 '_stat_axis_name',
 '_stat_axis_number',
 '_take_with_is_copy',
 '_to_dict_of_blocks',
 '_try_aggregate_string_function',
 '_typ',
 '_unpickle_series_compat',
 '_update_inplace',
 '_validate_dtype',
 '_values',
 '_where',
 '_xs',
 'abs',
 'add',
 'add_prefix',
 'add_suffix',
 'agg',
 'aggregate',
 'align',
 'all',
 'any',
 'append',
 'apply',
 'argmax',
 'argmin',
 'argsort',
 'array',
 'asfreq',
 'asof',
 'astype',
 'at',
 'at_time',
 'attrs',
 'autocorr',
 'axes',
 'between',
 'between_time',
 'bfill',
 'bool',
 'clip',
 'combine',
 'combine_first',
 'convert_dtypes',
 'copy',
 'corr',
 'count',
 'cov',
 'cummax',
 'cummin',
 'cumprod',
 'cumsum',
 'describe',
 'diff',
 'div',
 'divide',
 'divmod',
 'dot',
 'drop',
 'drop_duplicates',
 'droplevel',
 'dropna',
 'dtype',
 'dtypes',
 'duplicated',
 'empty',
 'eq',
 'equals',
 'ewm',
 'expanding',
 'explode',
 'factorize',
 'ffill',
 'fillna',
 'filter',
 'first',
 'first_valid_index',
 'floordiv',
 'ge',
 'get',
 'groupby',
 'gt',
 'hasnans',
 'head',
 'hist',
 'iat',
 'idxmax',
 'idxmin',
 'iloc',
 'index',
 'infer_objects',
 'interpolate',
 'is_monotonic',
 'is_monotonic_decreasing',
 'is_monotonic_increasing',
 'is_unique',
 'isin',
 'isna',
 'isnull',
 'item',
 'items',
 'iteritems',
 'keys',
 'kurt',
 'kurtosis',
 'last',
 'last_valid_index',
 'le',
 'loc',
 'lt',
 'mad',
 'map',
 'mask',
 'max',
 'mean',
 'median',
 'memory_usage',
 'min',
 'mod',
 'mode',
 'mul',
 'multiply',
 'name',
 'nbytes',
 'ndim',
 'ne',
 'nlargest',
 'notna',
 'notnull',
 'nsmallest',
 'nunique',
 'pct_change',
 'pipe',
 'plot',
 'pop',
 'pow',
 'prod',
 'product',
 'quantile',
 'radd',
 'rank',
 'ravel',
 'rdiv',
 'rdivmod',
 'reindex',
 'reindex_like',
 'rename',
 'rename_axis',
 'reorder_levels',
 'repeat',
 'replace',
 'resample',
 'reset_index',
 'rfloordiv',
 'rmod',
 'rmul',
 'rolling',
 'round',
 'rpow',
 'rsub',
 'rtruediv',
 'sample',
 'searchsorted',
 'sem',
 'set_axis',
 'shape',
 'shift',
 'size',
 'skew',
 'slice_shift',
 'sort_index',
 'sort_values',
 'squeeze',
 'std',
 'str',
 'sub',
 'subtract',
 'sum',
 'swapaxes',
 'swaplevel',
 'tail',
 'take',
 'to_clipboard',
 'to_csv',
 'to_dict',
 'to_excel',
 'to_frame',
 'to_hdf',
 'to_json',
 'to_latex',
 'to_list',
 'to_markdown',
 'to_numpy',
 'to_period',
 'to_pickle',
 'to_sql',
 'to_string',
 'to_timestamp',
 'to_xarray',
 'transform',
 'transpose',
 'truediv',
 'truncate',
 'tshift',
 'tz_convert',
 'tz_localize',
 'unique',
 'unstack',
 'update',
 'value_counts',
 'values',
 'var',
 'view',
 'where',
 'xs']

df['客舱'].head()

0     NaN
1     C85
2     NaN
3    C123
4     NaN
Name: 客舱, dtype: object

任务五：加载文件"test_1.csv"，然后对比"train.csv"，看看有哪些多出的列，然后将多出的列删除

test_1 = pd.read_csv("C:\\Users\\Administrator\\Documents\\DataScience\\hands-on-data-analysis\\第一单元项目集合\\test_1.csv")
test_1

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	a
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	100
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	100
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	100
3	3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	100
4	4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	100
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S	100
887	887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S	100
888	888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S	100
889	889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C	100
890	890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q	100

891 rows × 14 columns

test_1.drop(['a'],axis=1)

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 13 columns

任务六： 将[‘PassengerId’,‘Name’,‘Age’,‘Ticket’]这几个列元素隐藏，只观察其他几个列元素

df=pd.read_csv('train.csv')
df.drop(['PassengerId','Name','Age','Ticket'],axis=1)

	Survived	Pclass	Sex	SibSp	Parch	Fare	Cabin	Embarked
0	0	3	male	1	0	7.2500	NaN	S
1	1	1	female	1	0	71.2833	C85	C
2	1	3	female	0	0	7.9250	NaN	S
3	1	1	female	1	0	53.1000	C123	S
4	0	3	male	0	0	8.0500	NaN	S
...	...	...	...	...	...	...	...	...
886	0	2	male	0	0	13.0000	NaN	S
887	1	1	female	0	0	30.0000	B42	S
888	0	3	female	1	2	23.4500	NaN	S
889	1	1	male	0	0	30.0000	C148	C
890	0	3	male	0	0	7.7500	NaN	Q

891 rows × 8 columns

2.2筛选的逻辑

任务一： 我们以"Age"为筛选条件，显示年龄在10岁以下的乘客信息。

df[df['Age']<10]

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.00	3	1	349909	21.0750	NaN	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.00	1	1	PP 9549	16.7000	G6	S
16	17	0	3	Rice, Master. Eugene	male	2.00	4	1	382652	29.1250	NaN	Q
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.00	3	1	349909	21.0750	NaN	S
43	44	1	2	Laroche, Miss. Simonne Marie Anne Andree	female	3.00	1	2	SC/Paris 2123	41.5792	NaN	C
...	...	...	...	...	...	...	...	...	...	...	...	...
827	828	1	2	Mallet, Master. Andre	male	1.00	0	2	S.C./PARIS 2079	37.0042	NaN	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S
850	851	0	3	Andersson, Master. Sigvard Harald Elias	male	4.00	4	2	347082	31.2750	NaN	S
852	853	0	3	Boulos, Miss. Nourelain	female	9.00	1	1	2678	15.2458	NaN	C
869	870	1	3	Johnson, Master. Harold Theodor	male	4.00	1	1	347742	11.1333	NaN	S

62 rows × 12 columns

任务二： 以"Age"为条件，将年龄在10岁以上和50岁以下的乘客信息显示出来，并将这个数据命名为midage

midage = df[(df['Age']>10)&(df['Age']<50)]
midage.head()

	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

连接两个逻辑条件需要用括号括起来

任务三：将midage的数据中第100行的"Pclass"和"Sex"的数据显示出来

print(midage.iloc[100]['Pclass'])
print(midage.iloc[100]['Sex'])

2
male

还可以写作 midage.loc[[100],[‘Pclass’,‘Sex’]]

任务四：使用loc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

midage.loc[[100,105,108],['Pclass','Name','Sex']]

	Pclass	Name	Sex
100	3	Petranec, Miss. Matilda	female
105	3	Mionoff, Mr. Stoytcho	male
108	3	Rekic, Mr. Tido	male

任务五：使用iloc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

midage.iloc[[100,105,108],[2,3,4]] #无法用列名索引吗

	Pclass	Name	Sex
149	2	Byles, Rev. Thomas Roussel Davids	male
160	3	Cribb, Mr. John Hatfield	male
163	3	Calic, Mr. Jovo	male

3.1开始之前，导入numpy、pandas包和数据

text = pd.read_csv('train.chinese.csv')
text.head()

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
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

任务一：利用Pandas对示例数据进行排序，要求升序

frame = pd.DataFrame(np.arange(8).reshape((2, 4)), 
    index=['2', '1'], 
    columns=['d', 'a', 'b', 'c'])

frame.sort_index()

	d	a	b	c
1	4	5	6	7
2	0	1	2	3

frame.sort_index(axis=1) #按照列索引升序排列 a-b-c-d

	a	b	c	d
2	1	2	3	0
1	5	6	7	4

frame.sort_index(axis=1,ascending=False) #降序拍了

	d	c	b	a
2	0	3	2	1
1	4	7	6	5

frame.sort_values(by=['a','c'])

	d	a	b	c
2	0	1	2	3
1	4	5	6	7

任务二：对泰坦尼克号数据（trian.csv）按票价和年龄两列进行综合排序（降序排列），从数据中你能发现什么

df.sort_values(['Age','Fare'],ascending=False)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0000	A23	S
851	852	0	3	Svensson, Mr. Johan	male	74.0	0	0	347060	7.7750	NaN	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
116	117	0	3	Connors, Mr. Patrick	male	70.5	0	0	370369	7.7500	NaN	Q
...	...	...	...	...	...	...	...	...	...	...	...	...
481	482	0	2	Frost, Mr. Anthony Wood "Archie"	male	NaN	0	0	239854	0.0000	NaN	S
633	634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0000	NaN	S
674	675	0	2	Watson, Mr. Ennis Hastings	male	NaN	0	0	239856	0.0000	NaN	S
732	733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0000	NaN	S
815	816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0000	B102	S

891 rows × 12 columns

df.sort_values(['Fare','Age'],ascending=False)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S
341	342	1	1	Fortune, Miss. Alice Elizabeth	female	24.0	3	2	19950	263.0000	C23 C25 C27	S
...	...	...	...	...	...	...	...	...	...	...	...	...
481	482	0	2	Frost, Mr. Anthony Wood "Archie"	male	NaN	0	0	239854	0.0000	NaN	S
633	634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0000	NaN	S
674	675	0	2	Watson, Mr. Ennis Hastings	male	NaN	0	0	239856	0.0000	NaN	S
732	733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0000	NaN	S
815	816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0000	B102	S

891 rows × 12 columns

任务三：利用Pandas进行算术计算，计算两个DataFrame数据相加结果

frame1_a = pd.DataFrame(np.arange(9.).reshape(3, 3),
    columns=['a', 'b', 'c'],
    index=['one', 'two', 'three'])
frame1_b = pd.DataFrame(np.arange(12.).reshape(4, 3),
    columns=['a', 'e', 'c'],
    index=['first', 'one', 'two', 'second'])
frame1_a

	a	b	c
one	0.0	1.0	2.0
two	3.0	4.0	5.0
three	6.0	7.0	8.0

frame1_b

	a	e	c
first	0.0	1.0	2.0
one	3.0	4.0	5.0
two	6.0	7.0	8.0
second	9.0	10.0	11.0

frame1_a + frame1_b #结果会自动合并

	a	b	c	e
first	NaN	NaN	NaN	NaN
one	3.0	NaN	7.0	NaN
second	NaN	NaN	NaN	NaN
three	NaN	NaN	NaN	NaN
two	9.0	NaN	13.0	NaN

两个DataFrame相加后，会返回一个新的DataFrame，对应的行和列的值会相加，没有对应的会变成空值NaN。

任务四：通过泰坦尼克号数据如何计算出在船上最大的家族有多少人？

max(text['兄弟姐妹个数']+text['父母子女个数'])

10

任务五：学会使用Pandasdescribe()函数查看数据基本统计信息

frame2 = pd.DataFrame([[1.4, np.nan], 
    [7.1, -4.5],
    [np.nan, np.nan], 
    [0.75, -1.3]
    ], index=['a', 'b', 'c', 'd'], columns=['one', 'two'])
frame2

	one	two
a	1.40	NaN
b	7.10	-4.5
c	NaN	NaN
d	0.75	-1.3

frame.describe()

	d	a	b	c
count	2.000000	2.000000	2.000000	2.000000
mean	2.000000	3.000000	4.000000	5.000000
std	2.828427	2.828427	2.828427	2.828427
min	0.000000	1.000000	2.000000	3.000000
25%	1.000000	2.000000	3.000000	4.000000
50%	2.000000	3.000000	4.000000	5.000000
75%	3.000000	4.000000	5.000000	6.000000
max	4.000000	5.000000	6.000000	7.000000

count : 样本数据大小
mean : 样本数据的平均值
std : 样本数据的标准差
min : 样本数据的最小值
25% : 样本数据25%的时候的值
50% : 样本数据50%的时候的值
75% : 样本数据75%的时候的值
max : 样本数据的最大值

任务六：分别看看泰坦尼克号数据集中 票价、父母子女 这列数据的基本统计数据，你能发现什么？

text['票价'].describe()

count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: 票价, dtype: float64

text['父母子女个数'].describe()

count    891.000000
mean       0.381594
std        0.806057
min        0.000000
25%        0.000000
50%        0.000000
75%        0.000000
max        6.000000
Name: 父母子女个数, dtype: float64

``