Pandas discards missing data values

In Pandas, you can use dropna()the function to remove missing values. By default, the function drops rows containing any missing values. Here are some examples:

remove all rows with missing values

import pandas as pd

# 创建一个包含缺失值的 DataFrame
df = pd.DataFrame({'A': [1, 2, None, 4], 'B': [None, 6, 7, 8]})

# 删除所有包含缺失值的行
df = df.dropna()

print(df)

output:

     A    B
1  2.0  6.0

Delete only rows with missing values in a column

import pandas as pd

# 创建一个包含缺失值的 DataFrame
df = pd.DataFrame({'A': [1, 2, None, 4], 'B': [5, None, 7, 8]})

# 删除列 B 中包含缺失值的行
df = df.dropna(subset=['B'])

print(df)

output:

     A    B
0  1.0  5.0
2  NaN  7.0
3  4.0  8.0

It should be noted that dropna()the default parameter of axis=0means to delete rows, how='any'which means to delete as long as it contains any missing values, and thresh=Nonemeans that there is no limit to the number of deleted rows. Also, you can use to inplace=Trueapply delete operations to the original DataFrame without returning a new DataFrame object. For example: df.dropna(inplace=True).

pandas filter the value of a specific date

In Pandas, datetime indexes can be used to filter data for a specific date. First, you need to convert the date column in the DataFrame to a datetime type and set it as an index, and then you can use the datetime index to filter the data. Here are some examples:

Convert date column to datetime type

import pandas as pd

# 创建一个包含日期列和数值列的 DataFrame
df = pd.DataFrame({'date': ['2022-01-01', '2022-01-02', '2022-01-03'],
                   'value': [10, 20, 30]})

# 将日期列转换为日期时间类型
df['date'] = pd.to_datetime(df['date'])

# 将日期列设置为索引
df = df.set_index('date')

print(df)

output:

            value
date             
2022-01-01     10
2022-01-02     20
2022-01-03     30

Filter data for a specific date using a datetime index

# 筛选 2022 年 1 月 2 日的数据
df_filtered = df.loc['2022-01-02']

print(df_filtered)

output:

value    20
Name: 2022-01-02 00:00:00, dtype: int64

It should be noted that when using a datetime index, you need to ensure that the index of the DataFrame is a datetime type. Also, you can use loc[]to filter data within a certain time range, for example: df_filtered = df.loc['2022-01-01':'2022-01-02'], which will return all data between January 1, 2022 and January 2, 2022.

[Python] pandas discards missing data values and pandas filters values for specific dates

Pandas discards missing data values

pandas filter the value of a specific date

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